1
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Wang H, Nagarajan P, Winkler T, Bentley A, Miller C, Kraja A, Schwander K, Lee S, Wang W, Brown M, Morrison J, Giri A, O'Connell J, Bartz T, de Las Fuentes L, Gudmundsdottir V, Guo X, Harris S, Huang Z, Kals M, Kho M, Lefevre C, Luan J, Lyytikäinen LP, Mangino M, Milaneschi Y, Palmer N, Rao V, Rauramaa R, Shen B, Stadler S, Sun Q, Tang J, Thériault S, van der Graaf A, van der Most P, Wang Y, Weiss S, Westerman K, Yang Q, Yasuharu T, Zhao W, Zhu W, Altschul D, Ansari MAY, Anugu P, Argoty-Pantoja A, Arzt M, Aschard H, Attia J, Bazzano L, Breyer M, Brody J, Cade B, Chen HH, Chen YDI, Chen Z, de Vries P, Dimitrov L, Do A, Du J, Dupont C, Edwards T, Evans M, Faquih T, Felix S, Fisher-Hoch S, Floyd J, Graff M, Charles Gu C, Gu D, Hairston K, Hanley A, Heid I, Heikkinen S, Highland H, Hood M, Kähönen M, Karvonen-Gutierrez C, Kawaguchi T, Kazuya S, Tanika K, Komulainen P, Levy D, Lin H, Liu P, Marques-Vidal P, McCormick J, Mei H, Meigs J, Menni C, Nam K, Nolte I, Pacheco N, Petty L, Polikowsky H, Province M, Psaty B, Raffield L, Raitakari O, Rich S, Riha R, Risch L, Risch M, Ruiz-Narvaez E, Scott R, Sitlani C, Smith J, Sofer T, Teder-Laving M, Völker U, Vollenweider P, Wang G, van Dijk KWI, Wilson O, Xia R, Yao J, Young K, Zhang R, Zhu X, Below J, Böger C, Conen D, Cox S, Dörr M, Feitosa M, Fox E, Franceschini N, Gharib S, Gudnason V, Harlow S, He J, Holliday E, Kutalik Z, Lakka T, Lawlor D, Lee S, Lehtimäki T, Li C, Liu CT, Mägi R, Matsuda F, Morrison A, Penninx BWJH, Peyser P, Rotter J, Snieder H, Spector T, Wagenknecht L, Wareham N, Zonderman A, North K, Fornage M, Hung A, Manning A, Gauderman W, Chen H, Munroe P, Rao D, van Heemst D, Redline S, Noordam R. A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations. RESEARCH SQUARE 2024:rs.3.rs-4163414. [PMID: 39070651 PMCID: PMC11276021 DOI: 10.21203/rs.3.rs-4163414/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes. Investigating these genes' functional implications shed light on neurological, thyroidal, bone metabolism, and hematopoietic pathways that necessitate future investigation for blood pressure management that caters to sleep health lifestyle. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausible nature of distinct influences of both sleep duration extremes in cardiovascular health. Several of our loci are specific towards a particular population background or sex, emphasizing the importance of addressing heterogeneity entangled in gene-environment interactions, when considering precision medicine design approaches for blood pressure management.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Michael Brown
- The University of Texas Health Science Center at Houston
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- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai
| | | | | | | | | | - Quan Sun
- University of North Carolina, USA
| | | | | | | | | | | | - Stefan Weiss
- University Medicine Greifswald & University of Greifswald
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Sami Heikkinen
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus
| | | | | | | | | | | | | | | | | | | | | | | | | | - Joseph McCormick
- The University of Texas Health Science Center at Houston (UTHealth) School of Public Health
| | - Hao Mei
- University of Mississippi Medical Center
| | | | | | | | - Ilja Nolte
- University of Groningen, University Medical Center Groningen
| | | | | | | | | | | | | | - Olli Raitakari
- Turku University Hospital and Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku
| | | | | | | | | | | | - Rodney Scott
- University of Newcastle and the Hunter Medical Research Institute
| | | | | | | | | | | | | | | | | | | | - Rui Xia
- The Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Jiang He
- Tulane University School of Public Health and Tropical Medicine
| | | | | | | | | | | | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | | | | | | | | | | | - Patricia Peyser
- Department of Epidemiology, School of Public Health, University of Michigan
| | - Jerome Rotter
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center
| | | | | | | | | | | | | | - Myriam Fornage
- 1. Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center
- 2. Human Genetics Center, Department of Epidemiology, School of Public Health
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2
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Motsinger-Reif AA, Reif DM, Akhtari FS, House JS, Campbell CR, Messier KP, Fargo DC, Bowen TA, Nadadur SS, Schmitt CP, Pettibone KG, Balshaw DM, Lawler CP, Newton SA, Collman GW, Miller AK, Merrick BA, Cui Y, Anchang B, Harmon QE, McAllister KA, Woychik R. Gene-environment interactions within a precision environmental health framework. CELL GENOMICS 2024; 4:100591. [PMID: 38925123 PMCID: PMC11293590 DOI: 10.1016/j.xgen.2024.100591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/26/2024] [Accepted: 06/02/2024] [Indexed: 06/28/2024]
Abstract
Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.
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Affiliation(s)
- Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - John S House
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - C Ryan Campbell
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kyle P Messier
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA; Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David C Fargo
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Tiffany A Bowen
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Srikanth S Nadadur
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Charles P Schmitt
- Office of the Scientific Director, Office of Data Science, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kristianna G Pettibone
- Program Analysis Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David M Balshaw
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Cindy P Lawler
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Shelia A Newton
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Gwen W Collman
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Aubrey K Miller
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - B Alex Merrick
- Mechanistic Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Yuxia Cui
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Benedict Anchang
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Quaker E Harmon
- Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kimberly A McAllister
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Rick Woychik
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
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3
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Francis M, Westerman KE, Manning AK, Ye K. Gene-vegetarianism interactions in calcium, estimated glomerular filtration rate, and testosterone identified in genome-wide analysis across 30 biomarkers. PLoS Genet 2024; 20:e1011288. [PMID: 38990837 PMCID: PMC11239071 DOI: 10.1371/journal.pgen.1011288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 05/03/2024] [Indexed: 07/13/2024] Open
Abstract
We examined the associations of vegetarianism with metabolic biomarkers using traditional and genetic epidemiology. First, we addressed inconsistencies in self-reported vegetarianism among UK Biobank participants by utilizing data from two dietary surveys to find a cohort of strict European vegetarians (N = 2,312). Vegetarians were matched 1:4 with nonvegetarians for non-genetic association analyses, revealing significant effects of vegetarianism in 15 of 30 biomarkers. Cholesterol measures plus vitamin D were significantly lower in vegetarians, while triglycerides were higher. A genome-wide association study revealed no genome-wide significant (GWS; 5×10-8) associations with vegetarian behavior. We performed genome-wide gene-vegetarianism interaction analyses for the biomarkers, and detected a GWS interaction impacting calcium at rs72952628 (P = 4.47×10-8). rs72952628 is in MMAA, a B12 metabolic pathway gene; B12 has major deficiency potential in vegetarians. Gene-based interaction tests revealed two significant genes, RNF168 in testosterone (P = 1.45×10-6) and DOCK4 in estimated glomerular filtration rate (eGFR) (P = 6.76×10-7), which have previously been associated with testicular and renal traits, respectively. These nutrigenetic findings indicate genotype can modify the associations between vegetarianism and health outcomes.
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Affiliation(s)
- Michael Francis
- Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
| | - Kenneth E. Westerman
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, Massachusetts, United States of America
| | - Alisa K. Manning
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, Massachusetts, United States of America
| | - Kaixiong Ye
- Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
- Department of Genetics, University of Georgia, Athens, Georgia, United States of America
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4
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Tian Y, Lin Y, Qu C, Arndt V, Baurley JW, Berndt SI, Bien SA, Bishop DT, Brenner H, Buchanan DD, Budiarto A, Campbell PT, Carreras-Torres R, Casey G, Chan AT, Chen R, Chen X, Conti DV, Díez-Obrero V, Dimou N, Drew DA, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gunter MJ, Harlid S, Harrison TA, Hidaka A, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl KM, Joshi AD, Keku TO, Kawaguchi E, Kim AE, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Moreno V, Morrison J, Murphy N, Nan H, Nassir R, Newcomb PA, Obón-Santacana M, Ogino S, Ose J, Pardamean B, Pellatt AJ, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez EA, Sakoda LC, Schoen RE, Shcherbina A, Stern MC, Su YR, Thibodeau SN, Thomas DC, Tsilidis KK, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, White E, Wolk A, Woods MO, Wu AH, Peters U, Gauderman WJ, Hsu L, Chang-Claude J. Genetic risk impacts the association of menopausal hormone therapy with colorectal cancer risk. Br J Cancer 2024; 130:1687-1696. [PMID: 38561434 PMCID: PMC11091089 DOI: 10.1038/s41416-024-02638-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. METHODS We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. RESULTS The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10-8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%-4.0%) vs 6.1% (5.7%-6.5%) (difference 2.4%, P-value = 1.83 × 10-14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%-1.8%) vs 2.2% (1.9%-2.4%) (difference 0.6%, P-value = 1.01 × 10-3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. CONCLUSIONS MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.
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Affiliation(s)
- Yu Tian
- School of Public Health, Capital Medical University, Beijing, China
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - James W Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- BioRealm LLC, Walnut, CA, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3010, Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Robert Carreras-Torres
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute Dr Josep Trueta (IDIBGI), Salt, 17190, Girona, Spain
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Rui Chen
- School of Public Health, Capital Medical University, Beijing, China
| | - Xuechen Chen
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David V Conti
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Virginia Díez-Obrero
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Stephen B Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Sophia Harlid
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Kristina M Jordahl
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Amit D Joshi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA
| | - Eric Kawaguchi
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Andre E Kim
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Susanna C Larsson
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | | | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - Victor Moreno
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine and health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, Barcelona, Spain
| | - John Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Hongmei Nan
- Department of Global Health, Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indianapolis, Indianapolis, IN, USA
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura'a University, Mecca, Saudi Arabia
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Mireia Obón-Santacana
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Shuji Ogino
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Tokyo Medical and Dental University (Institute of Science Tokyo), Tokyo, Japan
| | - Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
- Hochschule Hannover, University of Applied Sciences and Arts, Department III: Media, Information and Design, Hannover, Germany
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Andrew J Pellatt
- Department of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anita R Peoples
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
- Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Ross L Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Edward A Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Anna Shcherbina
- Biomedical Informatics Program, Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Mariana C Stern
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Stephen N Thibodeau
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Duncan C Thomas
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | | | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, NL, Canada
| | - Anna H Wu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- University Cancer Centre Hamburg (UCCH), University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.
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5
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Papadimitriou N, Kim A, Kawaguchi ES, Morrison J, Diez-Obrero V, Albanes D, Berndt SI, Bézieau S, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Campbell PT, Carreras-Torres R, Chan AT, Chang-Claude J, Conti DV, Devall MA, Dimou N, Drew DA, Gruber SB, Harrison TA, Hoffmeister M, Huyghe JR, Joshi AD, Keku TO, Kundaje A, Küry S, Le Marchand L, Lewinger JP, Li L, Lynch BM, Moreno V, Newton CC, Obón-Santacana M, Ose J, Pellatt AJ, Peoples AR, Platz EA, Qu C, Rennert G, Ruiz-Narvaez E, Shcherbina A, Stern MC, Su YR, Thomas DC, Thomas CE, Tian Y, Tsilidis KK, Ulrich CM, Um CY, Visvanathan K, Wang J, White E, Woods MO, Schmit SL, Macrae F, Potter JD, Hopper JL, Peters U, Murphy N, Hsu L, Gunter MJ, Gauderman WJ. Genome-wide interaction study of dietary intake of fibre, fruits, and vegetables with risk of colorectal cancer. EBioMedicine 2024; 104:105146. [PMID: 38749303 PMCID: PMC11112268 DOI: 10.1016/j.ebiom.2024.105146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 04/15/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Consumption of fibre, fruits and vegetables have been linked with lower colorectal cancer (CRC) risk. A genome-wide gene-environment (G × E) analysis was performed to test whether genetic variants modify these associations. METHODS A pooled sample of 45 studies including up to 69,734 participants (cases: 29,896; controls: 39,838) of European ancestry were included. To identify G × E interactions, we used the traditional 1--degree-of-freedom (DF) G × E test and to improve power a 2-step procedure and a 3DF joint test that investigates the association between a genetic variant and dietary exposure, CRC risk and G × E interaction simultaneously. FINDINGS The 3-DF joint test revealed two significant loci with p-value <5 × 10-8. Rs4730274 close to the SLC26A3 gene showed an association with fibre (p-value: 2.4 × 10-3) and G × fibre interaction with CRC (OR per quartile of fibre increase = 0.87, 0.80, and 0.75 for CC, TC, and TT genotype, respectively; G × E p-value: 1.8 × 10-7). Rs1620977 in the NEGR1 gene showed an association with fruit intake (p-value: 1.0 × 10-8) and G × fruit interaction with CRC (OR per quartile of fruit increase = 0.75, 0.65, and 0.56 for AA, AG, and GG genotype, respectively; G × E -p-value: 0.029). INTERPRETATION We identified 2 loci associated with fibre and fruit intake that also modify the association of these dietary factors with CRC risk. Potential mechanisms include chronic inflammatory intestinal disorders, and gut function. However, further studies are needed for mechanistic validation and replication of findings. FUNDING National Institutes of Health, National Cancer Institute. Full funding details for the individual consortia are provided in acknowledgments.
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Affiliation(s)
- Nikos Papadimitriou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Andre Kim
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eric S Kawaguchi
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Virginia Diez-Obrero
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, 08908, Spain; Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Barcelona, 08908, Spain; Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stéphane Bézieau
- Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Emmanouil Bouras
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumour Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Australia; University of Melbourne Centre for Cancer Research, The University of Melbourne, Parkville, Australia; Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Robert Carreras-Torres
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, 08908, Spain; Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), 17190 Salt, Girona, Spain
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA; Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - David V Conti
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew A Devall
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA; Department of Public Health Sciences, Center for Public Health Genomics, Charlottesville, VA, USA
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Stephen B Gruber
- Department of Medical Oncology & Therapeutics Research and Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Amit D Joshi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Sébastien Küry
- Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France
| | | | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - Brigid M Lynch
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Victor Moreno
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, 08908, Spain; Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Barcelona, 08908, Spain; Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
| | - Christina C Newton
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Mireia Obón-Santacana
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, 08908, Spain; Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Barcelona, 08908, Spain
| | - Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, UT, USA; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Andrew J Pellatt
- Department of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anita R Peoples
- Huntsman Cancer Institute, Salt Lake City, UT, USA; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel; Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel; Clalit National Cancer Control Center, Haifa, Israel
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Anna Shcherbina
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Mariana C Stern
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Duncan C Thomas
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Claire E Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; School of Public Health, Capital Medical University, Beijing, China
| | - Konstantinos K Tsilidis
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece; Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK
| | - Cornelia M Ulrich
- Huntsman Cancer Institute, Salt Lake City, UT, USA; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Caroline Y Um
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jun Wang
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA; Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Finlay Macrae
- The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia; Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA.
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France; Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK.
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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6
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Drew DA, Kim AE, Lin Y, Qu C, Morrison J, Lewinger JP, Kawaguchi E, Wang J, Fu Y, Zemlianskaia N, Díez-Obrero V, Bien SA, Dimou N, Albanes D, Baurley JW, Wu AH, Buchanan DD, Potter JD, Prentice RL, Harlid S, Arndt V, Barry EL, Berndt SI, Bouras E, Brenner H, Budiarto A, Burnett-Hartman A, Campbell PT, Carreras-Torres R, Casey G, Chang-Claude J, Conti DV, Devall MA, Figueiredo JC, Gruber SB, Gsur A, Gunter MJ, Harrison TA, Hidaka A, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl KM, Kundaje A, Le Marchand L, Li L, Lynch BM, Murphy N, Nassir R, Newcomb PA, Newton CC, Obón-Santacana M, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Pellatt AJ, Peoples AR, Platz EA, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Stern MC, Su YR, Thomas DC, Tian Y, Tsilidis KK, Ulrich CM, Um CY, van Duijnhoven FJ, Van Guelpen B, White E, Hsu L, Moreno V, Peters U, Chan AT, Gauderman WJ. Two genome-wide interaction loci modify the association of nonsteroidal anti-inflammatory drugs with colorectal cancer. SCIENCE ADVANCES 2024; 10:eadk3121. [PMID: 38809988 PMCID: PMC11135391 DOI: 10.1126/sciadv.adk3121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 04/26/2024] [Indexed: 05/31/2024]
Abstract
Regular, long-term aspirin use may act synergistically with genetic variants, particularly those in mechanistically relevant pathways, to confer a protective effect on colorectal cancer (CRC) risk. We leveraged pooled data from 52 clinical trial, cohort, and case-control studies that included 30,806 CRC cases and 41,861 controls of European ancestry to conduct a genome-wide interaction scan between regular aspirin/nonsteroidal anti-inflammatory drug (NSAID) use and imputed genetic variants. After adjusting for multiple comparisons, we identified statistically significant interactions between regular aspirin/NSAID use and variants in 6q24.1 (top hit rs72833769), which has evidence of influencing expression of TBC1D7 (a subunit of the TSC1-TSC2 complex, a key regulator of MTOR activity), and variants in 5p13.1 (top hit rs350047), which is associated with expression of PTGER4 (codes a cell surface receptor directly involved in the mode of action of aspirin). Genetic variants with functional impact may modulate the chemopreventive effect of regular aspirin use, and our study identifies putative previously unidentified targets for additional mechanistic interrogation.
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Affiliation(s)
- David A. Drew
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Andre E. Kim
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - John Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eric Kawaguchi
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jun Wang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Yubo Fu
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Natalia Zemlianskaia
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Virginia Díez-Obrero
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Stephanie A. Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - James W. Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- BioRealm LLC, Walnut, CA, USA
| | - Anna H. Wu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Daniel D. Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria 3010 Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria 3010 Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - John D. Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Ross L. Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Sophia Harlid
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elizabeth L. Barry
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Emmanouil Bouras
- Laboratory of Hygiene, Social & Preventive Medicine and Medical Statistics, Department of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | | | - Peter T. Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Robert Carreras-Torres
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, 17190 Girona, Spain
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - David V. Conti
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew A.M. Devall
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - Jane C. Figueiredo
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Stephen B. Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
- Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Andrea Gsur
- Center for Cancer Research, Medical University Vienna, Vienna, Austria
| | - Marc J. Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK
| | - Tabitha A. Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R. Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Mark A. Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kristina M. Jordahl
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
- UVA Comprehensive Cancer Center, Charlottesville, VA, USA
| | - Brigid M. Lynch
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura’a University, Mecca, Saudi Arabia
| | - Polly A. Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- School of Public Health, University of Washington, Seattle, WA, USA
| | | | - Mireia Obón-Santacana
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L’Hospitalet del Llobregat, 08908 Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Rish K. Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Julie R. Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Nikos Papadimitriou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Andrew J. Pellatt
- Department of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anita R. Peoples
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Elizabeth A. Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Lori C. Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Peter C. Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Stephanie L. Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Robert E. Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Mariana C. Stern
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Yu-Ru Su
- Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Duncan C. Thomas
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- School of Public Health, Capital Medical University, Beijing, China
| | - Konstantinos K. Tsilidis
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Cornelia M. Ulrich
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Caroline Y. Um
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | | | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Victor Moreno
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L’Hospitalet del Llobregat, 08908 Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L’Hospitalet de Llobregat, 08908 Barcelona, Spain
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Andrew T. Chan
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - W. James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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7
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Herrera-Luis E, Benke K, Volk H, Ladd-Acosta C, Wojcik GL. Gene-environment interactions in human health. Nat Rev Genet 2024:10.1038/s41576-024-00731-z. [PMID: 38806721 DOI: 10.1038/s41576-024-00731-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/03/2024] [Indexed: 05/30/2024]
Abstract
Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.
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Affiliation(s)
- Esther Herrera-Luis
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kelly Benke
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Heather Volk
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Christine Ladd-Acosta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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8
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Westerman KE, Sofer T. Many roads to a gene-environment interaction. Am J Hum Genet 2024; 111:626-635. [PMID: 38579668 PMCID: PMC11023920 DOI: 10.1016/j.ajhg.2024.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 04/07/2024] Open
Abstract
Despite the importance of gene-environment interactions (GxEs) in improving and operationalizing genetic discovery, interpretation of any GxEs that are discovered can be surprisingly difficult. There are many potential biological and statistical explanations for a statistically significant finding and, likewise, it is not always clear what can be claimed based on a null result. A better understanding of the possible underlying mechanisms leading to a detected GxE can help investigators decide which are and which are not relevant to their hypothesis. Here, we provide a detailed explanation of five "phenomena," or data-generating mechanisms, that can lead to nonzero interaction estimates, as well as a discussion of specific instances in which they might be relevant. We hope that, given this framework, investigators can design more targeted experiments and provide cleaner interpretations of the associated results.
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Affiliation(s)
- Kenneth E Westerman
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA; Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Tamar Sofer
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
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9
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Veller C, Coop GM. Interpreting population- and family-based genome-wide association studies in the presence of confounding. PLoS Biol 2024; 22:e3002511. [PMID: 38603516 PMCID: PMC11008796 DOI: 10.1371/journal.pbio.3002511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 01/19/2024] [Indexed: 04/13/2024] Open
Abstract
A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.
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Affiliation(s)
- Carl Veller
- Department of Ecology & Evolution, University of Chicago, Chicago, Illinois, United States of America
| | - Graham M. Coop
- Department of Evolution and Ecology, and Center for Population Biology, University of California, Davis, California, United States of America
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10
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Alp E, Doguizi S, Mutlu Icduygu F, Akgun E, Sekeroglu MA, Ozer MA. An analysis of the relationship between ABCC8 and KCNJ11 gene polymorphisms and diabetic retinopathy in Turkish population. Ophthalmic Genet 2024; 45:126-132. [PMID: 38411150 DOI: 10.1080/13816810.2024.2317279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 02/06/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND Diabetic retinopathy (DR) occurs due to high blood glucose damage to the retina and leads to blindness if left untreated. KATP and related genes (KCNJ11 and ABCC8) play an important role in insulin secretion by glucose-stimulated pancreatic beta cells and the regulation of insulin secretion. KCNJ11 E23K (rs5219), ABCC8-3 C/T (rs1799854), Thr759Thr (rs1801261) and Arg1273Arg (rs1799859) are among the possible related single nucleotide polymorphisms (SNPs). The aim of this study is to find out how DR and these SNPs are associated with one another in the Turkish population. MATERIALS AND METHODS This study included 176 patients with type 2 diabetes mellitus without retinopathy (T2DM-rp), 177 DR patients, and 204 controls. Genomic DNA was extracted from whole blood, and genotypes were determined by the PCR-RFLP method. RESULTS In the present study, a significant difference was not found between all the groups in terms of Arg1273Arg polymorphism located in the ABCC8 gene. The T allele and the TT genotype in the -3 C/T polymorphism in this gene may have a protective effect in the development of DR (p = 0.036 for the TT genotype; p = 0.034 for T allele) and PDR (p = 0.042 and 0.025 for the TT genotype). The AA genotype showed a significant increase in the DR group compared to T2DM-rp in the KCNJ11 E23K polymorphism (p = 0.046). CONCLUSIONS Consequently, the T allele and TT genotype in the -3 C/T polymorphism of the ABCC8 gene may have a protective marker on the development of DR and PDR, while the AA genotype in the E23K polymorphism of the KCNJ11 gene may be effective in the development of DR in the Turkish population.
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Affiliation(s)
- Ebru Alp
- Faculty of Medicine, Department of Medical Biology, Giresun University, Giresun, Turkey
| | - Sibel Doguizi
- Department of Ophthalmology, Ulucanlar Eye Education and Research Hospital, Ministry of Health University, Ankara, Turkey
| | - Fadime Mutlu Icduygu
- Faculty of Medicine, Department of Medical Genetics, Giresun University, Giresun, Turkey
| | - Egemen Akgun
- Faculty of Medicine, Department of Medical Biology, Giresun University, Giresun, Turkey
| | - Mehmet Ali Sekeroglu
- Department of Ophthalmology, Ulucanlar Eye Education and Research Hospital, Ministry of Health University, Ankara, Turkey
| | - Murat Atabey Ozer
- Faculty of Medicine, Department of Ophthalmology, Giresun University, Giresun, Turkey
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11
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Nagarajan P, Winkler TW, Bentley AR, Miller CL, Kraja AT, Schwander K, Lee S, Wang W, Brown MR, Morrison JL, Giri A, O’Connell JR, Bartz TM, de las Fuentes L, Gudmundsdottir V, Guo X, Harris SE, Huang Z, Kals M, Kho M, Lefevre C, Luan J, Lyytikäinen LP, Mangino M, Milaneschi Y, Palmer ND, Rao V, Rauramaa R, Shen B, Stadler S, Sun Q, Tang J, Thériault S, van der Graaf A, van der Most PJ, Wang Y, Weiss S, Westerman KE, Yang Q, Yasuharu T, Zhao W, Zhu W, Altschul D, Ansari MAY, Anugu P, Argoty-Pantoja AD, Arzt M, Aschard H, Attia JR, Bazzanno L, Breyer MA, Brody JA, Cade BE, Chen HH, Ida Chen YD, Chen Z, de Vries PS, Dimitrov LM, Do A, Du J, Dupont CT, Edwards TL, Evans MK, Faquih T, Felix SB, Fisher-Hoch SP, Floyd JS, Graff M, Gu C, Gu D, Hairston KG, Hanley AJ, Heid IM, Heikkinen S, Highland HM, Hood MM, Kähönen M, Karvonen-Gutierrez CA, Kawaguchi T, Kazuya S, Kelly TN, Komulainen P, Levy D, Lin HJ, Liu PY, Marques-Vidal P, McCormick JB, Mei H, Meigs JB, Menni C, Nam K, Nolte IM, Pacheco NL, Petty LE, Polikowsky HG, Province MA, Psaty BM, Raffield LM, Raitakari OT, Rich SS, Riha RL, Risch L, Risch M, Ruiz-Narvaez EA, Scott RJ, Sitlani CM, Smith JA, Sofer T, Teder-Laving M, Völker U, Vollenweider P, Wang G, van Dijk KW, Wilson OD, Xia R, Yao J, Young KL, Zhang R, Zhu X, Below JE, Böger CA, Conen D, Cox SR, Dörr M, Feitosa MF, Fox ER, Franceschini N, Gharib SA, Gudnason V, Harlow SD, He J, Holliday EG, Kutalik Z, Lakka TA, Lawlor DA, Lee S, Lehtimäki T, Li C, Liu CT, Mägi R, Matsuda F, Morrison AC, Penninx BWJH, Peyser PA, Rotter JI, Snieder H, Spector TD, Wagenknecht LE, Wareham NJ, Zonderman AB, North KE, Fornage M, Hung AM, Manning AK, Gauderman J, Chen H, Munroe PB, Rao DC, van Heemst D, Redline S, Noordam R, Wang H. A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.07.24303870. [PMID: 38496537 PMCID: PMC10942520 DOI: 10.1101/2024.03.07.24303870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to genes involved in neurological, thyroidal, bone metabolism, and hematopoietic pathways. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausibility of distinct influences of both sleep duration extremes in cardiovascular health. With several of our loci reflecting specificity towards population background or sex, our discovery sheds light on the importance of embracing granularity when addressing heterogeneity entangled in gene-environment interactions, and in therapeutic design approaches for blood pressure management.
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Affiliation(s)
- Pavithra Nagarajan
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, US National Institutes of Health, Bethesda, MD, USA
| | - Clint L Miller
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesvil le, VA, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville ,VA, USA
| | - Aldi T Kraja
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Karen Schwander
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Songmi Lee
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
| | - Wenyi Wang
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - John L Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Ayush Giri
- Division of Quantitative Sciences, Department of Obstetrics & Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626), Department of Veterans Affairs/ Nashville, TN, USA
| | - Jeffrey R O’Connell
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Lisa de las Fuentes
- Cardiovascular Division, Department of Medicine, Washington University School of Medicine in St. Louis, MO, USA
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
| | - Valborg Gudmundsdottir
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, Department of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Sarah E Harris
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, US
| | - Mart Kals
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Minjung Kho
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Christophe Lefevre
- Department of Data Sciences, Hunter Medical Research Institute, New Lambton Heights, NSW, Australia
| | - Jian’an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Massimo Mangino
- Department of Twin Research, King’s College London, London, UK
- National Heart & Lung Institute, Cardiovascular Genomics and Precision Medicine, Imperial College London, London, UK
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam UMC/Vrije universiteit, Amsterdam, Netherlands
- GGZ inGeest, Amsterdam, Netherlands
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Varun Rao
- Division of Nephrology, Department of Medicine, University of Illinois Chicago, Chicago, USA
| | - Rainer Rauramaa
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Botong Shen
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Stefan Stadler
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jingxian Tang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Sébastien Thériault
- Department of Molecular Biology, Medical Biochemistry and Pathology, Université Laval, Quebec City, Qc, Canada
| | - Adriaan van der Graaf
- Statistical Genetics Group, Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Peter J van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Yujie Wang
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stefan Weiss
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Kenneth E Westerman
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
| | - Qian Yang
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tabara Yasuharu
- Graduate School of Public Health, Shizuoka Graduate University of Public Health, Shizuoka, Japan
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Wei Zhao
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Wanying Zhu
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Drew Altschul
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Md Abu Yusuf Ansari
- Department of Data Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Pramod Anugu
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, USA
| | - Anna D Argoty-Pantoja
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Michael Arzt
- Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany
| | - Hugues Aschard
- Department of Computational Biology, F-75015 Paris, France Institut Pasteur, Université Paris Cité, Paris, France
- Department of Epidemiology, Harvard TH School of Public Health, Boston, MA, USA
| | - John R Attia
- School of Medicine and Public Health, College of Health Medicine and Wellbeing, University of Newcastle, New Lambton Heights, NSW, Australia
| | - Lydia Bazzanno
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, US
| | - Max A Breyer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Hung-hsin Chen
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Zekai Chen
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Latchezar M Dimitrov
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Anh Do
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
| | - Jiawen Du
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Charles T Dupont
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Todd L Edwards
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626), Department of Veterans Affairs/ Nashville, TN, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, US A
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Tariq Faquih
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Stephan B Felix
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine, Department of Internal Medicine B, Un iversity Medicine Greifswald, Greifswald, Germany
| | - Susan P Fisher-Hoch
- School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Brownsville, TX, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Mariaelisa Graff
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Charles Gu
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
| | - Dongfeng Gu
- Shenzhen Key Laboratory of Cardiovascular Health and Precision Medicine, Southern University of Science an d Technology, Shenzhen, China
| | - Kristen G Hairston
- Department of Endocrinology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Anthony J Hanley
- Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Sami Heikkinen
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Kuopio
| | - Heather M Highland
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michelle M Hood
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Mika Kähönen
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
| | | | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Setoh Kazuya
- Graduate School of Public Health, Shizuoka Graduate University of Public Health, Shizuoka, Japan
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, University of Illinois Chicago, Chicago, USA
| | | | - Daniel Levy
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Henry J Lin
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Peter Y Liu
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Joseph B McCormick
- School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Brownsville, TX, USA
| | - Hao Mei
- Department of Data Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - James B Meigs
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Cristina Menni
- Department of Twin Research, King’s College London, London, UK
| | - Kisung Nam
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Natasha L Pacheco
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Lauren E Petty
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hannah G Polikowsky
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, and Department of Clinical Physiology and Nuclear Medicine, University of Turku, and Turku University Hospital, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Renata L Riha
- Department of Sleep Medicine, The University of Edinburgh, Edinburgh, UK
| | - Lorenz Risch
- Faculty of Medical Sciences , Institute for Laboratory Medicine, Private University in the Principality of Liecht enstein, Vaduz, Liechtenstein
- Center of Laboratory Medicine, Institute of Clinical Chemistry, University of Bern and Inselspital, Bern, Switze rland
| | - Martin Risch
- Central Laboratory, Cantonal Hospital Graubünden, Chur, Switzerland
- Medical Laboratory, Dr. Risch Anstalt, Vaduz, Liechtenstein
| | | | - Rodney J Scott
- School of Biomedical Sciences and Pharmacy, College of Health Medicine and Wellbeing, University of Newcastle, New Lambton Heights, NSW, Australia
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Uwe Völker
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Guanchao Wang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden, Netherlands
| | - Otis D Wilson
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626), Department of Veterans Affairs/ Nashville, TN, USA
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rui Xia
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Kristin L Young
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, US
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jennifer E Below
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Carsten A Böger
- Department of Nephrology, University Hospital Regensburg, Regensburg, Germany
- Department of Nephrology and Rheumatology, Kliniken Südostbayern, Traunstein, Germany
- KfH Kidney Centre Traunstein, Traunstein, Germany
| | - David Conen
- Population Health Research Institute, Medicine, McMaster University, Hamilton, On, Canada
| | - Simon R Cox
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Marcus Dörr
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Cardiology, Pneumology, Infectious Diseases, Intensive Care Medicine, Department of Internal Medicine B, Un iversity Medicine Greifswald, Greifswald, Germany
| | - Mary F Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | - Ervin R Fox
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Nora Franceschini
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sina A Gharib
- Pulmonary, Critical Care and Sleep Medicine, Medicine, University of Washington, Seattle, WA, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, Department of Health Sciences, University of Iceland, Reykjavik, Iceland
| | - Sioban D Harlow
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, US
- Tulane University Translational Sciences Institute, New Orleans, LA , USA
| | - Elizabeth G Holliday
- School of Medicine and Public Health, College of Health Medicine and Wellbeing, University of Newcastle, New Lambton Heights, NSW, Australia
| | - Zoltan Kutalik
- Statistical Genetics Group, Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | - Timo A Lakka
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Kuopio
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Finnish Cardiovascular Research Center - Tampere, Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere, Finland
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, US
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Brenda WJH Penninx
- Department of Psychiatry, Amsterdam UMC/Vrije universiteit, Amsterdam, Netherlands
- GGZ inGeest, Amsterdam, Netherlands
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
| | - Tim D Spector
- Department of Twin Research, King’s College London, London, UK
| | - Lynne E Wagenknecht
- Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | | | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Kari E North
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
- Human Genetics Center, Department of Epidemiology, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | | | - Adriana M Hung
- Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626), Department of Veterans Affairs/ Nashville, TN, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Alisa K Manning
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Patricia B Munroe
- Clinical Pharmacology and Precision Medicine, Queen Mary University of London, London, UK
| | - Dabeeru C Rao
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science, and Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Lei den, Netherlands
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Lei den, Netherlands
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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12
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Adams C, Manouchehrinia A, Quach HL, Quach DL, Olsson T, Kockum I, Schaefer C, Ponting CP, Alfredsson L, Barcellos LF. Evidence supports a causal association between allele-specific vitamin D receptor binding and multiple sclerosis among Europeans. Proc Natl Acad Sci U S A 2024; 121:e2302259121. [PMID: 38346204 PMCID: PMC10895341 DOI: 10.1073/pnas.2302259121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 12/11/2023] [Indexed: 02/15/2024] Open
Abstract
Although evidence exists for a causal association between 25-hydroxyvitamin D (25(OH)D) serum levels, and multiple sclerosis (MS), the role of variation in vitamin D receptor (VDR) binding in MS is unknown. Here, we leveraged previously identified variants associated with allele imbalance in VDR binding (VDR-binding variant; VDR-BV) in ChIP-exo data from calcitriol-stimulated lymphoblastoid cell lines and 25(OH)D serum levels from genome-wide association studies to construct genetic instrumental variables (GIVs). GIVs are composed of one or more genetic variants that serve as proxies for exposures of interest. Here, GIVs for both VDR-BVs and 25(OH)D were used in a two-sample Mendelian Randomization study to investigate the relationship between VDR binding at a locus, 25(OH)D serum levels, and MS risk. Data for 13,598 MS cases and 38,887 controls of European ancestry from Kaiser Permanente Northern California, Swedish MS studies, and the UK Biobank were included. We estimated the association between each VDR-BV GIV and MS. Significant interaction between a VDR-BV GIV and a GIV for serum 25OH(D) was evidence for a causal association between VDR-BVs and MS unbiased by pleiotropy. We observed evidence for associations between two VDR-BVs (rs2881514, rs2531804) and MS after correction for multiple tests. There was evidence of interaction between rs2881514 and a 25(OH)D GIV, providing evidence of a causal association between rs2881514 and MS. This study is the first to demonstrate evidence that variation in VDR binding at a locus contributes to MS risk. Our results are relevant to other autoimmune diseases in which vitamin D plays a role.
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Affiliation(s)
- Cameron Adams
- Genetic Epidemiology and Genomics Laboratory, School of Public Health, University of California, Berkeley, CA94720
| | - Ali Manouchehrinia
- Division of Neuro, Department of Clinical Neuroscience, Karolinska Institutet, StockholmSE-171 77, Sweden
- The Karolinska Neuroimmunology & Multiple Sclerosis Centre, Centrum for Molecular Medicine, Karolinska University Hospital, StockholmSE-171 77, Sweden
| | - Hong L. Quach
- Genetic Epidemiology and Genomics Laboratory, School of Public Health, University of California, Berkeley, CA94720
| | - Diana L. Quach
- Genetic Epidemiology and Genomics Laboratory, School of Public Health, University of California, Berkeley, CA94720
| | - Tomas Olsson
- Division of Neuro, Department of Clinical Neuroscience, Karolinska Institutet, StockholmSE-171 77, Sweden
- The Karolinska Neuroimmunology & Multiple Sclerosis Centre, Centrum for Molecular Medicine, Karolinska University Hospital, StockholmSE-171 77, Sweden
- Academic Specialist Center, Stockholm113 65, Sweden
| | - Ingrid Kockum
- Division of Neuro, Department of Clinical Neuroscience, Karolinska Institutet, StockholmSE-171 77, Sweden
- The Karolinska Neuroimmunology & Multiple Sclerosis Centre, Centrum for Molecular Medicine, Karolinska University Hospital, StockholmSE-171 77, Sweden
- Academic Specialist Center, Stockholm113 65, Sweden
| | - Catherine Schaefer
- Kaiser Permanente Division of Research, Kaiser Permanente Northern California, Oakland, CA94612
| | - Chris P. Ponting
- Medical Research Council Human Genetics Unit, The Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, EdinburghEH4 2XU, United Kingdom
| | - Lars Alfredsson
- Division of Neuro, Department of Clinical Neuroscience, Karolinska Institutet, StockholmSE-171 77, Sweden
- Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm113 65, Sweden
- Institute of Environmental Medicine, Karolinska Institutet, StockholmSE-171 77, Sweden
| | - Lisa F. Barcellos
- Genetic Epidemiology and Genomics Laboratory, School of Public Health, University of California, Berkeley, CA94720
- Kaiser Permanente Division of Research, Kaiser Permanente Northern California, Oakland, CA94612
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13
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Westerman KE, Kilpeläinen TO, Sevilla-Gonzalez M, Connelly MA, Wood AC, Tsai MY, Taylor KD, Rich SS, Rotter JI, Otvos JD, Bentley AR, Mora S, Aschard H, Rao DC, Gu C, Chasman DI, Manning AK. Refinement of a published gene-physical activity interaction impacting HDL-cholesterol: role of sex and lipoprotein subfractions. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.23.24301689. [PMID: 38313294 PMCID: PMC10836120 DOI: 10.1101/2024.01.23.24301689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Large-scale gene-environment interaction (GxE) discovery efforts often involve compromises in the definition of outcomes and choice of covariates for the sake of data harmonization and statistical power. Consequently, refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C). This GxE was originally identified by Kilpeläinen et al., with the strongest cohort-specific signal coming from the Women's Genome Health Study (WGHS). We thus explored this GxE further in the WGHS (N = 23,294), with follow-up in the UK Biobank (UKB; N = 281,380), and the Multi-Ethnic Study of Atherosclerosis (MESA; N = 4,587). Self-reported PA (MET-hrs/wk), genotypes at rs295849 (nearest gene: LHX1), and NMR metabolomics data were available in all three cohorts. As originally reported, minor allele carriers of rs295849 in WGHS had a stronger positive association between PA and HDL-C (pint = 0.002). When testing a range of NMR metabolites (primarily lipoprotein and lipid subfractions) to refine the HDL-C outcome, we found a stronger interaction effect on medium-sized HDL particle concentrations (M-HDL-P; pint = 1.0×10-4) than HDL-C. Meta-regression revealed a systematically larger interaction effect in cohorts from the original meta-analysis with a greater fraction of women (p = 0.018). In the UKB, GxE effects were stronger both in women and using M-HDL-P as the outcome. In MESA, the primary interaction for HDL-C showed nominal significance (pint = 0.013), but without clear differences by sex and with a greater magnitude using large, rather than medium, HDL-P as an outcome. Towards reconciling these observations, further exploration leveraging NMR platform-specific HDL subfraction diameter annotations revealed modest agreement across all cohorts in the interaction affecting medium-to-large particles. Taken together, our work provides additional insights into a specific known gene-PA interaction while illustrating the importance of phenotype and model refinement towards understanding and replicating GxEs.
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Affiliation(s)
- Kenneth E. Westerman
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tuomas O. Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Magdalena Sevilla-Gonzalez
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Alexis C. Wood
- USDA/ARS Children’s Nutrition Center, Baylor College of Medicine, Houston, TX, USA
| | - Michael Y. Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Kent D. Taylor
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - James D. Otvos
- Lipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Amy R. Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Samia Mora
- Center for Lipid Metabolomics, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA, USA
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université de Paris, Paris, FR
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - DC Rao
- Division of Biostatistics, Washington University, St. Louis, MO, USA
| | - Charles Gu
- Division of Biostatistics, Washington University, St. Louis, MO, USA
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Genetics, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alisa K. Manning
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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14
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Miao J, Wu Y, Lu Q. Statistical methods for gene-environment interaction analysis. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL STATISTICS 2024; 16:e1635. [PMID: 38699459 PMCID: PMC11064894 DOI: 10.1002/wics.1635] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/12/2023] [Indexed: 05/05/2024]
Abstract
Most human complex phenotypes result from multiple genetic and environmental factors and their interactions. Understanding the mechanisms by which genetic and environmental factors interact offers valuable insights into the genetic architecture of complex traits and holds great potential for advancing precision medicine. The emergence of large population biobanks has led to the development of numerous statistical methods aiming at identifying gene-environment interactions (G × E). In this review, we present state-of-the-art statistical methodologies for G × E analysis. We will survey a spectrum of approaches for single-variant G × E mapping, followed by various techniques for polygenic G × E analysis. We conclude this review with a discussion on the future directions and challenges in G × E research.
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Affiliation(s)
- Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Yixuan Wu
- University of Wisconsin–Madison, Madison, Wisconsin, USA
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Department of Statistics, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, Wisconsin, USA
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15
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Prone-Olazabal D, Davies I, González-Galarza FF. Metabolic Syndrome: An Overview on Its Genetic Associations and Gene-Diet Interactions. Metab Syndr Relat Disord 2023; 21:545-560. [PMID: 37816229 DOI: 10.1089/met.2023.0125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2023] Open
Abstract
Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors that includes central obesity, hyperglycemia, hypertension, and dyslipidemias and whose inter-related occurrence may increase the odds of developing type 2 diabetes and cardiovascular diseases. MetS has become one of the most studied conditions, nevertheless, due to its complex etiology, this has not been fully elucidated. Recent evidence describes that both genetic and environmental factors play an important role on its development. With the advent of genomic-wide association studies, single nucleotide polymorphisms (SNPs) have gained special importance. In this review, we present an update of the genetics surrounding MetS as a single entity as well as its corresponding risk factors, considering SNPs and gene-diet interactions related to cardiometabolic markers. In this study, we focus on the conceptual aspects, diagnostic criteria, as well as the role of genetics, particularly on SNPs and polygenic risk scores (PRS) for interindividual analysis. In addition, this review highlights future perspectives of personalized nutrition with regard to the approach of MetS and how individualized multiomics approaches could improve the current outlook.
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Affiliation(s)
- Denisse Prone-Olazabal
- Postgraduate Department, Faculty of Medicine, Autonomous University of Coahuila, Torreon, Mexico
| | - Ian Davies
- Research Institute of Sport and Exercise Science, The Institute for Health Research, Liverpool John Moores University, Liverpool, United Kingdom
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16
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Hurwitz LM, Beane Freeman LE, Andreotti G, Hofmann JN, Parks CG, Sandler DP, Lubin JH, Liu J, Jones K, Berndt SI, Koutros S. Joint associations between established genetic susceptibility loci, pesticide exposures, and risk of prostate cancer. ENVIRONMENTAL RESEARCH 2023; 237:117063. [PMID: 37659638 PMCID: PMC10591852 DOI: 10.1016/j.envres.2023.117063] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/20/2023] [Accepted: 08/31/2023] [Indexed: 09/04/2023]
Abstract
More than 200 genetic variants have been independently associated with prostate cancer risk. Studies among farmers have also observed increased prostate cancer risk associated with exposure to specific organophosphate (fonofos, terbufos, malathion, dimethoate) and organochlorine (aldrin, chlordane) insecticides. We examined the joint associations between these pesticides, established prostate cancer loci, and prostate cancer risk among 1,162 cases (588 aggressive) and 2,206 frequency-matched controls nested in the Agricultural Health Study cohort. History of lifetime pesticide use was combined with a polygenic risk score (PRS) generated using 256 established prostate cancer risk variants. Logistic regression models estimated the joint associations of the pesticides, the PRS, and the 256 individual genetic variants with risk of total and aggressive prostate cancer. Likelihood ratio tests assessed multiplicative interaction. We observed interaction between ever use of fonofos and the PRS in relation to total and aggressive prostate cancer risk. Compared to the reference group (never use, PRS < median), men with ever use of fonofos and PRS > median had elevated risks of total (OR 1.35 [1.06-1.73], p-interaction = 0.03) and aggressive (OR 1.49 [1.09-2.04], p-interaction = 0.19) prostate cancer. There was also suggestion of interaction between pesticides and individual genetic variants occurring in regions associated with DNA damage response (CDH3, EMSY genes) and with variants related to altered androgen receptor-driven transcriptional programs critical for prostate cancer. Our study provides evidence that men with greater genetic susceptibility to prostate cancer may be at higher risk if they are also exposed to pesticides and suggests potential mechanisms by which pesticides may increase prostate cancer risk.
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Affiliation(s)
- Lauren M Hurwitz
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, USA.
| | - Laura E Beane Freeman
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, USA
| | - Gabriella Andreotti
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, USA
| | - Jonathan N Hofmann
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, USA
| | - Christine G Parks
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA
| | - Jay H Lubin
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, USA
| | - Jia Liu
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, USA; Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Kristine Jones
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, USA; Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sonja I Berndt
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, USA
| | - Stella Koutros
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD, USA
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17
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Ibrahim S, Gaborit B, Lenoir M, Collod-Beroud G, Stefanovic S. Maternal Pre-Existing Diabetes: A Non-Inherited Risk Factor for Congenital Cardiopathies. Int J Mol Sci 2023; 24:16258. [PMID: 38003449 PMCID: PMC10671602 DOI: 10.3390/ijms242216258] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 11/26/2023] Open
Abstract
Congenital heart defects (CHDs) are the most common form of birth defects in humans. They occur in 9 out of 1000 live births and are defined as structural abnormalities of the heart. Understanding CHDs is difficult due to the heterogeneity of the disease and its multifactorial etiology. Advances in genomic sequencing have made it possible to identify the genetic factors involved in CHDs. However, genetic origins have only been found in a minority of CHD cases, suggesting the contribution of non-inherited (environmental) risk factors to the etiology of CHDs. Maternal pregestational diabetes is associated with a three- to five-fold increased risk of congenital cardiopathies, but the underlying molecular mechanisms are incompletely understood. According to current hypotheses, hyperglycemia is the main teratogenic agent in diabetic pregnancies. It is thought to induce cell damage, directly through genetic and epigenetic dysregulations and/or indirectly through production of reactive oxygen species (ROS). The purpose of this review is to summarize key findings on the molecular mechanisms altered in cardiac development during exposure to hyperglycemic conditions in utero. It also presents the various in vivo and in vitro techniques used to experimentally model pregestational diabetes. Finally, new approaches are suggested to broaden our understanding of the subject and develop new prevention strategies.
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Affiliation(s)
- Stéphanie Ibrahim
- Aix Marseille University, INSERM, INRAE, C2VN, 13005 Marseille, France;
| | - Bénédicte Gaborit
- Department of Endocrinology, Metabolic Diseases and Nutrition, Pôle ENDO, APHM, 13005 Marseille, France
| | - Marien Lenoir
- Department of Congenital Heart Surgery, La Timone Children Hospital, APHM, Aix Marseille University, 13005 Marseille, France
| | | | - Sonia Stefanovic
- Aix Marseille University, INSERM, INRAE, C2VN, 13005 Marseille, France;
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18
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Aglago EK, Kim A, Lin Y, Qu C, Evangelou M, Ren Y, Morrison J, Albanes D, Arndt V, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Chen X, Conti DV, Devall M, Diez-Obrero V, Dimou N, Drew D, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Hidaka A, Harrison TA, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl K, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obón-Santacana M, Moreno V, Murphy N, Nan H, Nassir R, Newcomb PA, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su YR, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Wang J, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Hsu L, Gauderman WJ, Peters U, Tsilidis KK, Campbell PT. A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk. Cancer Res 2023; 83:2572-2583. [PMID: 37249599 PMCID: PMC10391330 DOI: 10.1158/0008-5472.can-22-3713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/25/2023] [Accepted: 05/24/2023] [Indexed: 05/31/2023]
Abstract
Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer. SIGNIFICANCE This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.
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Affiliation(s)
- Elom K. Aglago
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom
| | - Andre Kim
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Marina Evangelou
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom
| | - Yu Ren
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom
| | - John Morrison
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elizabeth L. Barry
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - James W. Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- BioRealm LLC, Walnut, California
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephanie A. Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - D. Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, United Kingdom
| | - Emmanouil Bouras
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel D. Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
| | - Robert Carreras-Torres
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, Girona, Spain
| | - Graham Casey
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia
| | - Tjeng Wawan Cenggoro
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Andrew T. Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - Xuechen Chen
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - David V. Conti
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Matthew Devall
- Department of Family Medicine, University of Virginia, Charlottesville, Virginia
| | - Virginia Diez-Obrero
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - David Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Jane C. Figueiredo
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Graham G. Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Stephen B. Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte California
| | - Andrea Gsur
- Center for Cancer Research, Medical University of Vienna, Vienna, Austria
| | - Marc J. Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Heather Hampel
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte California
| | - Sophia Harlid
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Tabitha A. Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R. Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Mark A. Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Kristina Jordahl
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Amit D. Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Eric S. Kawaguchi
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Temitope O. Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, California
- Department of Computer Science, Stanford University, Stanford, California
| | - Susanna C. Larsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Juan Pablo Lewinger
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, Virginia
| | - Brigid M. Lynch
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
- Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
| | - Bharuno Mahesworo
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Marko Mandic
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Mireia Obón-Santacana
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Victor Moreno
- ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Hongmei Nan
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indianapolis, Indiana
- IU Melvin and Bren Simon Cancer Center, Indiana University, Indianapolis, Indiana
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura'a University, Mecca, Saudi Arabia
| | - Polly A. Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Shuji Ogino
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
| | - Rish K. Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, Arizona
| | - Julie R. Palmer
- Department of Medicine, Boston University School of Medicine, Slone Epidemiology Center, Boston University, Boston, Massachusetts
| | - Nikos Papadimitriou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Anita R. Peoples
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
| | - Elizabeth A. Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - John D. Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
- Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Ross L. Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Lori C. Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Peter C. Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio
| | | | - Robert E. Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Anna Shcherbina
- Department of Genetics, Stanford University, Stanford, California
- Department of Computer Science, Stanford University, Stanford, California
| | - Martha L. Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah
| | - Mariana C. Stern
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Catherine M. Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Stephen N. Thibodeau
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Duncan C. Thomas
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- School of Public Health, Capital Medical University, Beijing, China
| | - Cornelia M. Ulrich
- Huntsman Cancer Institute, Salt Lake City, Utah
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
| | - Franzel JB van Duijnhoven
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, the Netherlands
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Jun Wang
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Michael O. Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada
| | - Anna H. Wu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Natalia Zemlianskaia
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Biostatistics, University of Washington, Seattle, Washington
| | - W. James Gauderman
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington
- Department of Epidemiology, University of Washington School of Public Health, Seattle, Washington
| | - Konstantinos K. Tsilidis
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, United Kingdom
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Peter T. Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York
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19
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Lahtinen H, Moustgaard H, Ripatti S, Martikainen P. Association between genetic risk of alcohol consumption and alcohol-related morbidity and mortality under different alcohol policy conditions: Evidence from the Finnish alcohol price reduction of 2004. Addiction 2023; 118:678-685. [PMID: 36564914 DOI: 10.1111/add.16118] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 12/05/2022] [Indexed: 12/25/2022]
Abstract
AIMS Harmful alcohol consumption is influenced by both genetic susceptibility and the price of alcohol. Many previous studies have observed that genetic susceptibility to consumption of alcohol is more predictive in less restrictive drinking conditions. We assess whether such a pattern applies when the prices of alcoholic beverages are decreased. DESIGN Data consist of genetically informed population-representative surveys (FINRISK 1992, 1997, 2002 and Health 2000) linked to administrative registers. We analysed the interaction between a polygenic score (PGS) for alcoholic drinks per week consumed and price reduction in predicting the incidence of alcohol-related hospitalizations and deaths in difference-in-difference and interrupted time-series frameworks. SETTING Individuals in Finland were followed quarter-yearly from 1 March 2000 to 31 May 2008. PARTICIPANTS A total of 22 152 individuals (607 132-person quarter-years, 1399 outcome events) aged 30-79 years. INTERVENTION A natural experiment stemming from the alcohol tax reduction in March 2004 and import deregulation in May 2004. MEASUREMENTS Outcome was quarter-yearly-measured alcohol-related death or hospitalization. The independent variables of main interest were PGS and a price reform indicator. We adjusted for gender, age, age squared, season, 10 first principal components of the genome, data collection round and genotyping batch. FINDINGS Both alcohol price reduction and one standard deviation change in PGS were associated with alcohol-related health outcomes; odds ratios (ORs) were 1.32, 95% confidence interval (CI) = 1.13, 1.53 and 1.26, 95% CI = 1.12, 1.42 in the 8-year follow-up, respectively. The association between PGS and alcohol-related morbidity was similar before and after the alcohol price reform (PGS × price reform interaction OR = 0.96, 95% CI = 0.81, 1.14). These results were robust across different follow-up periods and measurement and analysis strategies. CONCLUSIONS Although the decrease of alcohol price in Finland in 2004 substantially increased overall alcohol-related morbidity and mortality, the genetic susceptibility to alcohol consumption did not become more manifest in predicting them.
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Affiliation(s)
- Hannu Lahtinen
- Faculty of Social Sciences, University of Helsinki, Helsinki, Finland
| | - Heta Moustgaard
- Helsinki Institute for Social Sciences and Humanities, University of Helsinki, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Pekka Martikainen
- Population Research Unit, University of Helsinki, Helsinki, Finland.,Max Planck Institute for Demographic Research, Rostock, Germany
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20
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Carreras-Torres R, Kim AE, Lin Y, Diez-Obrero V, Bien SA, Qu C, Wang J, Dimou N, Aglago EK, Albanes D, Arndt V, Baurley JW, Berndt SI, Bézieau S, Bishop DT, Bouras E, Brenner H, Budiarto A, Campbell PT, Casey G, Chan AT, Chang-Claude J, Chen X, Conti DV, Dampier CH, Devall MAM, Drew DA, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Harrison TA, Hidaka A, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl KM, Kawaguchi E, Keku TO, Kundaje A, Le Marchand L, Lewinger JP, Li L, Mahesworo B, Morrison JL, Murphy N, Nan H, Nassir R, Newcomb PA, Obón-Santacana M, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Pharoah PDP, Platz EA, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su YR, Tangen CM, Thomas DC, Tian Y, Tsilidis KK, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Cenggoro TW, Weinstein SJ, White E, Wolk A, Woods MO, Hsu L, Peters U, Moreno V, Gauderman WJ. Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response. Cancer Epidemiol Biomarkers Prev 2023; 32:315-328. [PMID: 36576985 PMCID: PMC9992283 DOI: 10.1158/1055-9965.epi-22-0763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/19/2022] [Accepted: 12/19/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Tobacco smoking is an established risk factor for colorectal cancer. However, genetically defined population subgroups may have increased susceptibility to smoking-related effects on colorectal cancer. METHODS A genome-wide interaction scan was performed including 33,756 colorectal cancer cases and 44,346 controls from three genetic consortia. RESULTS Evidence of an interaction was observed between smoking status (ever vs. never smokers) and a locus on 3p12.1 (rs9880919, P = 4.58 × 10-8), with higher associated risk in subjects carrying the GG genotype [OR, 1.25; 95% confidence interval (CI), 1.20-1.30] compared with the other genotypes (OR <1.17 for GA and AA). Among ever smokers, we observed interactions between smoking intensity (increase in 10 cigarettes smoked per day) and two loci on 6p21.33 (rs4151657, P = 1.72 × 10-8) and 8q24.23 (rs7005722, P = 2.88 × 10-8). Subjects carrying the rs4151657 TT genotype showed higher risk (OR, 1.12; 95% CI, 1.09-1.16) compared with the other genotypes (OR <1.06 for TC and CC). Similarly, higher risk was observed among subjects carrying the rs7005722 AA genotype (OR, 1.17; 95% CI, 1.07-1.28) compared with the other genotypes (OR <1.13 for AC and CC). Functional annotation revealed that SNPs in 3p12.1 and 6p21.33 loci were located in regulatory regions, and were associated with expression levels of nearby genes. Genetic models predicting gene expression revealed that smoking parameters were associated with lower colorectal cancer risk with higher expression levels of CADM2 (3p12.1) and ATF6B (6p21.33). CONCLUSIONS Our study identified novel genetic loci that may modulate the risk for colorectal cancer of smoking status and intensity, linked to tumor suppression and immune response. IMPACT These findings can guide potential prevention treatments.
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Affiliation(s)
- Robert Carreras-Torres
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, 17190, Girona, Spain
| | - Andre E Kim
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Virginia Diez-Obrero
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Jun Wang
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Elom K Aglago
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - James W Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Stéphane Bézieau
- Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Emmanouil Bouras
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Peter T Campbell
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, Georgia, USA
| | - Graham Casey
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Xuechen Chen
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David V Conti
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Christopher H Dampier
- Department of General Surgery, University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Matthew AM Devall
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - David A Drew
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Stephen B Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kristina M Jordahl
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Eric Kawaguchi
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Anshul Kundaje
- Department of Genetics, Department of Computer Science, Stanford University, Stanford, California, USA
| | | | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Bharuno Mahesworo
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - John L Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Hongmei Nan
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura’a University, Saudi Arabia
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Mireia Obón-Santacana
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Shuji Ogino
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA; Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, Utah, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, Arizona, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Nikos Papadimitriou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | | | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Peter C Scacheri
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, Ohio, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, Ohio, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Anna Shcherbina
- Biomedical Informatics Program, Dept. of Biomedical Data Sciences, Stanford University
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - Mariana C Stern
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Duncan C Thomas
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- School of Public Health, Capital Medical University, Beijing, China
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Cornelia M Ulrich
- Huntsman Cancer Institute, Salt Lake City, Utah, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Franzel JB van Duijnhoven
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, and Biomedical Center, Medical Faculty, Pilsen, Czech Republic
| | - Tjeng Wawan Cenggoro
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- School of Public Health, University of Washington, Seattle, Washington, USA
| | - Victor Moreno
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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21
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Kawaguchi ES, Kim AE, Pablo Lewinger J, Gauderman WJ. Improved two-step testing of genome-wide gene-environment interactions. Genet Epidemiol 2023; 47:152-166. [PMID: 36571162 PMCID: PMC9974838 DOI: 10.1002/gepi.22509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 10/13/2022] [Accepted: 11/11/2022] [Indexed: 12/27/2022]
Abstract
Two-step tests for gene-environment (G × E $G\times E$ ) interactions exploit marginal single-nucleotide polymorphism (SNP) effects to improve the power of a genome-wide interaction scan. They combine a screening step based on marginal effects used to "bin" SNPs for weighted hypothesis testing in the second step to deliver greater power over single-step tests while preserving the genome-wide Type I error. However, the presence of many SNPs with detectable marginal effects on the trait of interest can reduce power by "displacing" true interactions with weaker marginal effects and by adding to the number of tests that need to be corrected for multiple testing. We introduce a new significance-based allocation into bins for Step-2G × E $G\times E$ testing that overcomes the displacement issue and propose a computationally efficient approach to account for multiple testing within bins. Simulation results demonstrate that these simple improvements can provide substantially greater power than current methods under several scenarios. An application to a multistudy collaboration for understanding colorectal cancer reveals a G × Sex interaction located near the SMAD7 gene.
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Affiliation(s)
- Eric S. Kawaguchi
- Department of Population and Public Health Sciences, University of Southern California, California, USA
| | - Andre E. Kim
- Department of Population and Public Health Sciences, University of Southern California, California, USA
| | - Juan Pablo Lewinger
- Department of Population and Public Health Sciences, University of Southern California, California, USA
| | - W. James Gauderman
- Department of Population and Public Health Sciences, University of Southern California, California, USA
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22
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Veller C, Coop G. Interpreting population and family-based genome-wide association studies in the presence of confounding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.26.530052. [PMID: 36909521 PMCID: PMC10002712 DOI: 10.1101/2023.02.26.530052] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding, and can also absorb the 'indirect' genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of Mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect size estimates are used in polygenic scores. We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. In addition to known biases that can arise in family-based GWASs when interactions between family members are ignored, we show that biases can also arise from gene-by-environment (G×E) interactions when parental genotypes are not distributed identically across interacting environmental and genetic backgrounds. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding and interactions.
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Affiliation(s)
- Carl Veller
- Department of Evolution and Ecology, and Center for Population Biology, University of California, Davis, CA 95616
| | - Graham Coop
- Department of Evolution and Ecology, and Center for Population Biology, University of California, Davis, CA 95616
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23
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Wang Z, Shi W, Carroll RJ, Chatterjee N. Joint Modeling of Gene-Environment Correlations and Interactions using Polygenic Risk Scores in Case-Control Studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.14.528572. [PMID: 36824704 PMCID: PMC9948994 DOI: 10.1101/2023.02.14.528572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Polygenic risk scores (PRS) are rapidly emerging as aggregated measures of disease-risk associated with many genetic variants. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using PRS in case-control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the two models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared to the standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis for gene-environment correlations. Finally, using simulated case-control datasets within the UK Biobank study, we demonstrate the power of the proposed method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.
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Affiliation(s)
- Ziqiao Wang
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Wen Shi
- McKusick-Nathans Institute, Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Raymond J. Carroll
- Department of Statistics, Texas A&M University, College Station, TX, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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24
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Efficient gene-environment interaction testing through bootstrap aggregating. Sci Rep 2023; 13:937. [PMID: 36650248 PMCID: PMC9845231 DOI: 10.1038/s41598-023-28172-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 01/13/2023] [Indexed: 01/18/2023] Open
Abstract
Gene-environment (GxE) interactions are an important and sophisticated component in the manifestation of complex phenotypes. Simple univariate tests lack statistical power due to the need for multiple testing adjustment and not incorporating potential interplay between several genetic loci. Approaches based on internally constructed genetic risk scores (GRS) require the partitioning of the available sample into training and testing data sets, thus, lowering the effective sample size for testing the GxE interaction itself. To overcome these issues, we propose a statistical test that employs bagging (bootstrap aggregating) in the GRS construction step and utilizes its out-of-bag prediction mechanism. This approach has the key advantage that the full available data set can be used for both constructing the GRS and testing the GxE interaction. To also incorporate interactions between genetic loci, we, furthermore, investigate if using random forests as the GRS construction method in GxE interaction testing further increases the statistical power. In a simulation study, we show that both novel procedures lead to a higher statistical power for detecting GxE interactions, while still controlling the type I error. The random-forests-based test outperforms a bagging-based test that uses the elastic net as its base learner in most scenarios. An application of the testing procedures to a real data set from a German cohort study suggests that there might be a GxE interaction involving exposure to air pollution regarding rheumatoid arthritis.
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25
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Heritability and Etiology: Heritability estimates can provide causally relevant information. PERSONALITY AND INDIVIDUAL DIFFERENCES 2023. [DOI: 10.1016/j.paid.2022.111896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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26
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Wattacheril JJ, Raj S, Knowles DA, Greally JM. Using epigenomics to understand cellular responses to environmental influences in diseases. PLoS Genet 2023; 19:e1010567. [PMID: 36656803 PMCID: PMC9851565 DOI: 10.1371/journal.pgen.1010567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
It is a generally accepted model that environmental influences can exert their effects, at least in part, by changing the molecular regulators of transcription that are described as epigenetic. As there is biochemical evidence that some epigenetic regulators of transcription can maintain their states long term and through cell division, an epigenetic model encompasses the idea of maintenance of the effect of an exposure long after it is no longer present. The evidence supporting this model is mostly from the observation of alterations of molecular regulators of transcription following exposures. With the understanding that the interpretation of these associations is more complex than originally recognised, this model may be oversimplistic; therefore, adopting novel perspectives and experimental approaches when examining how environmental exposures are linked to phenotypes may prove worthwhile. In this review, we have chosen to use the example of nonalcoholic fatty liver disease (NAFLD), a common, complex human disease with strong environmental and genetic influences. We describe how epigenomic approaches combined with emerging functional genetic and single-cell genomic techniques are poised to generate new insights into the pathogenesis of environmentally influenced human disease phenotypes exemplified by NAFLD.
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Affiliation(s)
- Julia J. Wattacheril
- Department of Medicine, Center for Liver Disease and Transplantation, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, New York, United States of America
| | - Srilakshmi Raj
- Division of Genomics, Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - David A. Knowles
- New York Genome Center, New York, New York, United States of America
- Department of Computer Science, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
| | - John M. Greally
- Division of Genomics, Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, United States of America
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27
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Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior. Mol Psychiatry 2023; 28:17-27. [PMID: 35790874 DOI: 10.1038/s41380-022-01669-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 06/02/2022] [Accepted: 06/09/2022] [Indexed: 01/07/2023]
Abstract
Individual differences in human brain structure, function, and behavior can be attributed to genetic variations, environmental exposures, and their interactions. Although genome-wide association studies have identified many genetic variants associated with brain imaging phenotypes, environmental exposures associated with these phenotypes remain largely unknown. Here, we propose that environmental neuroscience should pay more attention on exploring the associations between lifetime environmental exposures (exposome) and brain imaging phenotypes and identifying both cumulative environmental effects and their vulnerable age windows during the life course. Exposome-neuroimaging association studies face several challenges including the accurate measurement of the totality of environmental exposures varied in space and time, the highly correlated structure of the exposome, and the lack of standardized approaches for exposome-wide association studies. By agnostically scanning the effects of environmental exposures on brain imaging phenotypes and their interactions with genomic variations, exposome-neuroimaging association analyses will improve our understanding of causal factors associated with individual differences in brain structure and function as well as their relations with cognitive abilities and neuropsychiatric disorders.
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28
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Hecker J, Prokopenko D, Moll M, Lee S, Kim W, Qiao D, Voorhies K, Kim W, Vansteelandt S, Hobbs BD, Cho MH, Silverman EK, Lutz SM, DeMeo DL, Weiss ST, Lange C. A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables. PLoS Genet 2022; 18:e1010464. [PMID: 36383614 PMCID: PMC9668174 DOI: 10.1371/journal.pgen.1010464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022] Open
Abstract
The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.
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Affiliation(s)
- Julian Hecker
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Dmitry Prokopenko
- Harvard Medical School, Boston, Massachusetts, United States of America
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Matthew Moll
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Sanghun Lee
- Department of Medical Consilience, Division of Medicine, Graduate School, Dankook University, Yongin, South Korea
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Dandi Qiao
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kirsten Voorhies
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Pilgrim Health Care, Boston, Massachusetts, United States of America
| | - Woori Kim
- Harvard Medical School, Boston, Massachusetts, United States of America
- Systems Biology and Computer Science Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Stijn Vansteelandt
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Brian D. Hobbs
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Michael H. Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Sharon M. Lutz
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Pilgrim Health Care, Boston, Massachusetts, United States of America
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Scott T. Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Christoph Lange
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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Cultural evolution may influence heritability by shaping assortative mating. Behav Brain Sci 2022; 45:e181. [PMID: 36098444 DOI: 10.1017/s0140525x21001801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Uchiyama et al. productively discuss how culture can influence genetic heritability and, by modifying environmental conditions, limit the generalizability of genome-wide association studies (GWASs). Here, we supplement their account by highlighting how recent changes in culture and institutions in industrialized, westernized societies - such as increased female workforce participation - may have increased assortative mating. This alters the distribution of genotypes themselves, increasing heritability and phenotypic variance, and may be detectable using the latest methods.
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Tian Y, Kim AE, Bien SA, Lin Y, Qu C, Harrison TA, Carreras-Torres R, Díez-Obrero V, Dimou N, Drew DA, Hidaka A, Huyghe JR, Jordahl KM, Morrison J, Murphy N, Obón-Santacana M, Ulrich CM, Ose J, Peoples AR, Ruiz-Narvaez EA, Shcherbina A, Stern MC, Su YR, van Duijnhoven FJB, Arndt V, Baurley JW, Berndt SI, Bishop DT, Brenner H, Buchanan DD, Chan AT, Figueiredo JC, Gallinger S, Gruber SB, Harlid S, Hoffmeister M, Jenkins MA, Joshi AD, Keku TO, Larsson SC, Le Marchand L, Li L, Giles GG, Milne RL, Nan H, Nassir R, Ogino S, Budiarto A, Platz EA, Potter JD, Prentice RL, Rennert G, Sakoda LC, Schoen RE, Slattery ML, Thibodeau SN, Van Guelpen B, Visvanathan K, White E, Wolk A, Woods MO, Wu AH, Campbell PT, Casey G, Conti DV, Gunter MJ, Kundaje A, Lewinger JP, Moreno V, Newcomb PA, Pardamean B, Thomas DC, Tsilidis KK, Peters U, Gauderman WJ, Hsu L, Chang-Claude J. Genome-Wide Interaction Analysis of Genetic Variants With Menopausal Hormone Therapy for Colorectal Cancer Risk. J Natl Cancer Inst 2022; 114:1135-1148. [PMID: 35512400 PMCID: PMC9360460 DOI: 10.1093/jnci/djac094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/17/2022] [Accepted: 04/26/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND The use of menopausal hormone therapy (MHT) may interact with genetic variants to influence colorectal cancer (CRC) risk. METHODS We conducted a genome-wide, gene-environment interaction between single nucleotide polymorphisms and the use of any MHT, estrogen only, and combined estrogen-progestogen therapy with CRC risk, among 28 486 postmenopausal women (11 519 CRC patients and 16 967 participants without CRC) from 38 studies, using logistic regression, 2-step method, and 2- or 3-degree-of-freedom joint test. A set-based score test was applied for rare genetic variants. RESULTS The use of any MHT, estrogen only and estrogen-progestogen were associated with a reduced CRC risk (odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.64 to 0.78; OR = 0.65, 95% CI = 0.53 to 0.79; and OR = 0.73, 95% CI = 0.59 to 0.90, respectively). The 2-step method identified a statistically significant interaction between a GRIN2B variant rs117868593 and MHT use, whereby MHT-associated CRC risk was statistically significantly reduced in women with the GG genotype (OR = 0.68, 95% CI = 0.64 to 0.72) but not within strata of GC or CC genotypes. A statistically significant interaction between a DCBLD1 intronic variant at 6q22.1 (rs10782186) and MHT use was identified by the 2-degree-of-freedom joint test. The MHT-associated CRC risk was reduced with increasing number of rs10782186-C alleles, showing odds ratios of 0.78 (95% CI = 0.70 to 0.87) for TT, 0.68 (95% CI = 0.63 to 0.73) for TC, and 0.66 (95% CI = 0.60 to 0.74) for CC genotypes. In addition, 5 genes in rare variant analysis showed suggestive interactions with MHT (2-sided P < 1.2 × 10-4). CONCLUSION Genetic variants that modify the association between MHT and CRC risk were identified, offering new insights into pathways of CRC carcinogenesis and potential mechanisms involved.
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Affiliation(s)
- Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- School of Public Health, Capital Medical University, Beijing, China
| | - Andre E Kim
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Robert Carreras-Torres
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L’Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Virginia Díez-Obrero
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L’Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Akihisa Hidaka
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Kristina M Jordahl
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - John Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Mireia Obón-Santacana
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L’Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Cornelia M Ulrich
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Anita R Peoples
- Huntsman Cancer Institute, Salt Lake City, UT, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Edward A Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Anna Shcherbina
- Biomedical Informatics Program, Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Mariana C Stern
- Division of Biostatistics, Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | | | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - James W Baurley
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
- BioRealm LLC, Walnut, CA, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Stephen B Gruber
- Division of Biostatistics, Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sophia Harlid
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Amit D Joshi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA
| | - Susanna C Larsson
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | | | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Hongmei Nan
- Department of Global Health, Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA
- Department of Epidemiology, Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura’a University, Mecca, Saudi Arabia
| | - Shuji Ogino
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Arif Budiarto
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Ross L Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Stephen N Thibodeau
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John’s, NL,Canada
| | - Anna H Wu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Peter T Campbell
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - David V Conti
- Division of Biostatistics, Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Victor Moreno
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Barcelona, Spain
- Oncology Data Analytics Program, Catalan Institute of Oncology, L’Hospitalet de Llobregat, Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Duncan C Thomas
- Division of Biostatistics, Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences & USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Cancer Centre Hamburg (UCCH), University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
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Westerman KE, Majarian TD, Giulianini F, Jang DK, Miao J, Florez JC, Chen H, Chasman DI, Udler MS, Manning AK, Cole JB. Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers. Nat Commun 2022; 13:3993. [PMID: 35810165 PMCID: PMC9271055 DOI: 10.1038/s41467-022-31625-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 06/24/2022] [Indexed: 11/29/2022] Open
Abstract
Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p < 4.5×10-9). Most are concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicate (p < 0.05) in the Women's Genome Health Study (N = 23,294). Next, we test each locus-biomarker pair for interaction across 2380 exposures, identifying 847 significant interactions (p < 2.4×10-7), of which 132 are independent (p < 0.05) after accounting for correlation between exposures. Specific examples demonstrate interaction of triglyceride-associated variants with distinct body mass- versus body fat-related exposures as well as genotype-specific associations between alcohol consumption and liver stress at the ADH1B gene. Our catalog of variance-quantitative trait loci and gene-environment interactions is publicly available in an online portal.
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Affiliation(s)
- Kenneth E Westerman
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA.
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Timothy D Majarian
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Dong-Keun Jang
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jenkai Miao
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Miriam S Udler
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alisa K Manning
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Joanne B Cole
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
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Brandes N, Weissbrod O, Linial M. Open problems in human trait genetics. Genome Biol 2022; 23:131. [PMID: 35725481 PMCID: PMC9208223 DOI: 10.1186/s13059-022-02697-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 05/30/2022] [Indexed: 12/21/2022] Open
Abstract
Genetic studies of human traits have revolutionized our understanding of the variation between individuals, and yet, the genetics of most traits is still poorly understood. In this review, we highlight the major open problems that need to be solved, and by discussing these challenges provide a primer to the field. We cover general issues such as population structure, epistasis and gene-environment interactions, data-related issues such as ancestry diversity and rare genetic variants, and specific challenges related to heritability estimates, genetic association studies, and polygenic risk scores. We emphasize the interconnectedness of these problems and suggest promising avenues to address them.
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Affiliation(s)
- Nadav Brandes
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Omer Weissbrod
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Michal Linial
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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Gene-lifestyle interactions in the genomics of human complex traits. Eur J Hum Genet 2022; 30:730-739. [PMID: 35314805 PMCID: PMC9178041 DOI: 10.1038/s41431-022-01045-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 12/22/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.
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Yu L, Zhang H, Zheng T, Liu J, Fang X, Cao S, Xia W, Xu S, Li Y. Phthalate Exposure, PPARα Variants, and Neurocognitive Development of Children at Two Years. Front Genet 2022; 13:855544. [PMID: 35464856 PMCID: PMC9019295 DOI: 10.3389/fgene.2022.855544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 03/09/2022] [Indexed: 11/13/2022] Open
Abstract
Background: The PPARα gene may be crucial to the neurotoxic effect of phthalates. However, epidemiological studies considering the neurodevelopmental influence of phthalates interacting with genetic susceptibility are limited. We hypothesized phthalates could interact with the PPARα gene, synergistically affecting neurocognitive development. Methods: A total of 961 mother-infant pairs were involved in this study. The concentrations of phthalate metabolites in maternal urine during pregnancy were detected. Children’s neurocognitive development was estimated with the Bailey Infant Development Inventory (BSID). Genetic variations in PPARα were genotyped with the Illumina Asian Screening Array. We applied generalized linear regression models to estimate genotypes and phthalate metabolites’ association with children’s neurocognitive development. Results: After adjusting for potential confounders, the mono-n-butyl phthalate (MnBP) concentration was negatively associated with Psychomotor Development Index (PDI) (β = −0.86, 95% CI: −1.67, −0.04). The associations between MnBP and neurocognitive development might be modified by PPARα rs1800246. Compared with low-MnBP individuals carrying rs1800246 GG genotypes, high-MnBP individuals with the AG + AA genotype had a higher risk of neurocognitive developmental delay, with the odds ratio of 2.76 (95% CI:1.14, 6.24). Conclusions: Our current study revealed that prenatal exposure to MnBP was negatively correlated with children’s neurocognitive development, and PPARα rs1800246 might modify the association.
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Affiliation(s)
- Ling Yu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongling Zhang
- School of Health and Nursing, Wuchang University of Technology, Wuhan, China
| | - Tongzhang Zheng
- Department of Epidemiology, Brown University, Providence, RI, United States
| | - Juan Liu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xingjie Fang
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuting Cao
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Xia
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shunqing Xu
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuanyuan Li
- Key Laboratory of Environment and Health, Ministry of Education and Ministry of Environmental Protection, State Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Morton SU, Pereira AC, Quiat D, Richter F, Kitaygorodsky A, Hagen J, Bernstein D, Brueckner M, Goldmuntz E, Kim RW, Lifton RP, Porter GA, Tristani-Firouzi M, Chung WK, Roberts A, Gelb BD, Shen Y, Newburger JW, Seidman JG, Seidman CE. Genome-Wide De Novo Variants in Congenital Heart Disease Are Not Associated With Maternal Diabetes or Obesity. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2022; 15:e003500. [PMID: 35130025 PMCID: PMC9295870 DOI: 10.1161/circgen.121.003500] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Congenital heart disease (CHD) is the most common anomaly at birth, with a prevalence of ≈1%. While infants born to mothers with diabetes or obesity have a 2- to 3-fold increased incidence of CHD, the cause of the increase is unknown. Damaging de novo variants (DNV) in coding regions are more common among patients with CHD, but genome-wide rates of coding and noncoding DNVs associated with these prenatal exposures have not been studied in patients with CHD. METHODS DNV frequencies were determined for 1812 patients with CHD who had whole-genome sequencing and prenatal history data available from the Pediatric Cardiac Genomics Consortium's CHD GENES study (Genetic Network). The frequency of DNVs was compared between subgroups using t test or linear model. RESULTS Among 1812 patients with CHD, the number of DNVs per patient was higher with maternal diabetes (76.5 versus 72.1, t test P=3.03×10-11), but the difference was no longer significant after including parental ages in a linear model (paternal and maternal correction P=0.42). No interaction was observed between diabetes risk and parental age (paternal and maternal interaction P=0.80 and 0.68, respectively). No difference was seen in DNV count per patient based on maternal obesity (72.0 versus 72.2 for maternal body mass index <25 versus maternal body mass index >30, t test P=0.86). CONCLUSIONS After accounting for parental age, the offspring of diabetic or obese mothers have no increase in DNVs compared with other children with CHD. These results emphasize the role for other mechanisms in the cause of CHD associated with these prenatal exposures. REGISTRATION URL: https://clinicaltrials.gov; NCT01196182.
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Affiliation(s)
- Sarah U. Morton
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Boston, MA USA,Department of Pediatrics, Harvard Medical School, Boston, MA USA
| | | | - Daniel Quiat
- Department of Pediatrics, Harvard Medical School, Boston, MA USA,Department of Cardiology, Boston Children’s Hospital, Boston, MA USA
| | - Felix Richter
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Alexander Kitaygorodsky
- Departments of Systems Biology and Biomedical Informatics, Columbia University Medical Center, New York, NY USA
| | - Jacob Hagen
- Departments of Systems Biology and Biomedical Informatics, Columbia University Medical Center, New York, NY USA
| | - Daniel Bernstein
- Department of Pediatrics (Cardiology), Stanford University, Stanford, CA USA
| | - Martina Brueckner
- Departments of Genetics and Pediatrics; Yale University School of Medicine, New Haven, CT USA
| | - Elizabeth Goldmuntz
- Division of Cardiology, Children’s Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | | | - Richard P. Lifton
- Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY USA
| | - George A. Porter
- Department of Pediatrics, University of Rochester Medical Center, The School of Medicine and Dentistry, Rochester, NY USA
| | | | - Wendy K. Chung
- Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY USA
| | - Amy Roberts
- Department of Pediatrics, Harvard Medical School, Boston, MA USA,Department of Cardiology, Boston Children’s Hospital, Boston, MA USA
| | - Bruce D. Gelb
- Mindich Child Health and Development Institute and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Yufeng Shen
- Departments of Systems Biology and Biomedical Informatics, Columbia University Medical Center, New York, NY USA
| | - Jane W. Newburger
- Department of Pediatrics, Harvard Medical School, Boston, MA USA,Department of Cardiology, Boston Children’s Hospital, Boston, MA USA
| | - J. G. Seidman
- Department of Genetics, Harvard Medical School, Boston, MA USA
| | - Christine E. Seidman
- Department of Genetics, Harvard Medical School, Boston, MA USA,Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA USA,Howard Hughes Medical Institute, Chevy Chase, MD USA
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Huang Y, Hui Q, Gwinn M, Hu YJ, Quyyumi AA, Vaccarino V, Sun YV. Interaction between genetics and smoking in determining risk of coronary artery diseases. Genet Epidemiol 2022; 46:199-212. [PMID: 35170807 PMCID: PMC9086149 DOI: 10.1002/gepi.22446] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 11/18/2021] [Accepted: 01/20/2022] [Indexed: 12/15/2022]
Abstract
Coronary artery disease (CAD) is a preeminent cause of death, and smoking is a strong risk factor for CAD. Genetic factors contribute to the development of CAD, but the interplay between genetic predisposition and smoking history in CAD remains unclear. Using data from the UK Biobank, we constructed several genetic risk scores (GRSs) based on known CAD loci and assessed their interactions with smoking for the development of incident CAD in 307,147 participants of European ancestry who were free of CAD. We fitted Cox proportional hazard models and assessed gene-smoking interaction on both multiplicative and additive scales. Overall, we found no multiplicative interactions, but observed a synergistic additive interaction of GRS with both smoking status and pack-years of smoking, finding that the absolute CAD risk due to smoking was higher for those with high genetic risk. Trait-based sub-GRSs suggested smoking status and smoking intensity measured by pack-years might confer gene-smoking interaction effects with different intermediate risk factors for CAD. Our study results suggest that genetics could modify the effects of smoking on CAD and highlight the value of addressing gene-lifestyle interactions on both additive and multiplicative scales.
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Affiliation(s)
- Yunfeng Huang
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Qin Hui
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Marta Gwinn
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yi-Juan Hu
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Arshed A Quyyumi
- Division of Cardiology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Viola Vaccarino
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Yan V Sun
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA,Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA
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37
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Kawaguchi ES, Li G, Lewinger JP, Gauderman WJ. Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint. Stat Med 2022; 41:1644-1657. [PMID: 35075649 PMCID: PMC9007892 DOI: 10.1002/sim.9319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 11/10/2021] [Accepted: 12/26/2021] [Indexed: 01/13/2023]
Abstract
Defined by their genetic profile, individuals may exhibit differential clinical outcomes due to an environmental exposure. Identifying subgroups based on specific exposure-modifying genes can lead to targeted interventions and focused studies. Genome-wide interaction scans (GWIS) can be performed to identify such genes, but these scans typically suffer from low power due to the large multiple testing burden. We provide a novel framework for powerful two-step hypothesis tests for GWIS with a time-to-event endpoint under the Cox proportional hazards model. In the Cox regression setting, we develop an approach that prioritizes genes for Step-2 G × E testing based on a carefully constructed Step-1 screening procedure. Simulation results demonstrate this two-step approach can lead to substantially higher power for identifying gene-environment ( G × E ) interactions compared to the standard GWIS while preserving the family wise error rate over a range of scenarios. In a taxane-anthracycline chemotherapy study for breast cancer patients, the two-step approach identifies several gene expression by treatment interactions that would not be detected using the standard GWIS.
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Affiliation(s)
- Eric S Kawaguchi
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA
| | - Gang Li
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, California, USA.,Department of Computational Medicine, University of California, Los Angeles, Los Angeles, California, USA
| | - Juan Pablo Lewinger
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA
| | - W James Gauderman
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA
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38
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Ueki M, Tamiya G. Smooth-threshold multivariate genetic prediction incorporating gene–environment interactions. G3 GENES|GENOMES|GENETICS 2021; 11:6343458. [PMID: 34849749 PMCID: PMC8664495 DOI: 10.1093/g3journal/jkab278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 07/12/2021] [Indexed: 11/17/2022]
Abstract
We propose a genetic prediction modeling approach for genome-wide association study (GWAS) data that can include not only marginal gene effects but also gene–environment (GxE) interaction effects—i.e., multiplicative effects of environmental factors with genes rather than merely additive effects of each. The proposed approach is a straightforward extension of our previous multiple regression-based method, STMGP (smooth-threshold multivariate genetic prediction), with the new feature being that genome-wide test statistics from a GxE interaction analysis are used to weight the corresponding variants. We develop a simple univariate regression approximation to the GxE interaction effect that allows a direct fit of the STMGP framework without modification. The sparse nature of our model automatically removes irrelevant predictors (including variants and GxE combinations), and the model is able to simultaneously incorporate multiple environmental variables. Simulation studies to evaluate the proposed method in comparison with other modeling approaches demonstrate its superior performance under the presence of GxE interaction effects. We illustrate the usefulness of our prediction model through application to real GWAS data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).
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Affiliation(s)
- Masao Ueki
- School of Information and Data Sciences, Nagasaki University, Nagasaki 852-8521, Japan
| | - Gen Tamiya
- Tohoku University Graduate School of Medicine, Sendai, Miyagi 980-8575, Japan
- Statistical Genetics Team, RIKEN Center for Advanced Intelligence Project, Chuo-ku, Tokyo 103-0027, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi 980-8573, Japan
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39
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Jin Q, Shi G. Meta-Analysis of Joint Test of SNP and SNP-Environment Interaction with Heterogeneity. Hum Hered 2021; 86:1-9. [PMID: 34700323 DOI: 10.1159/000519098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 07/29/2021] [Indexed: 12/13/2022] Open
Abstract
Many complex diseases are caused by single nucleotide polymorphisms (SNPs), environmental factors, and the interaction between SNPs and environment. Joint tests of the SNP and SNP-environment interaction effects (JMA) and meta-regression (MR) are commonly used to evaluate these SNP-environment interactions. However, these two methods do not consider genetic heterogeneity. We previously presented a random-effect MR, which provided higher power than the MR in datasets with high heterogeneity. However, this method requires group-level data, which sometimes are not available. Given this, we designed this study to evaluate the introduction of the random effects of SNP and SNP-environment interaction into the JMA, and then extended this to the random effect model. Likelihood ratio statistic is applied to test the JMA and the new method we proposed in this paper. We evaluated the null distributions of these tests, and the powers for this method. This method was verified by simulation and was shown to provide similar powers to the random effect meta-regression method (RMR). However, this method only requires study-level data which relaxed the condition of the RMR. Our study suggests that this method is more suitable for finding the association between SNP and diseases in the absence of group-level data.
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Affiliation(s)
- Qinqin Jin
- State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China.,Applied Science College, Taiyuan University of Science and Technology, Taiyuan, China
| | - Gang Shi
- State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, China
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40
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Westerman KE, Miao J, Chasman DI, Florez JC, Chen H, Manning AK, Cole JB. Genome-wide gene-diet interaction analysis in the UK Biobank identifies novel effects on hemoglobin A1c. Hum Mol Genet 2021; 30:1773-1783. [PMID: 33864366 PMCID: PMC8411984 DOI: 10.1093/hmg/ddab109] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 03/26/2021] [Accepted: 04/13/2021] [Indexed: 01/10/2023] Open
Abstract
Diet is a significant modifiable risk factor for type 2 diabetes (T2D), and its effect on disease risk is under partial genetic control. Identification of specific gene-diet interactions (GDIs) influencing risk biomarkers such as glycated hemoglobin (HbA1c) is a critical step towards precision nutrition for T2D prevention, but progress has been slow due to limitations in sample size and accuracy of dietary exposure measurement. We leveraged the large UK Biobank (UKB) cohort and a diverse group of dietary exposures, including 30 individual dietary traits and 8 empirical dietary patterns, to conduct genome-wide interaction studies in ~340 000 European-ancestry participants to identify novel GDIs influencing HbA1c. We identified five variant-dietary trait pairs reaching genome-wide significance (P < 5 × 10-8): two involved dietary patterns (meat pattern with rs147678157 and a fruit & vegetable-based pattern with rs3010439) and three involved individual dietary traits (bread consumption with rs62218803, dried fruit consumption with rs140270534 and milk type [dairy vs. other] with 4:131148078_TAGAA_T). These were affected minimally by adjustment for geographical and lifestyle-related confounders, and four of the five variants lacked genetic main effects that would have allowed their detection in a traditional genome-wide association study for HbA1c. Notably, multiple loci near transient receptor potential subfamily M genes (TRPM2 and TRPM3) interacted with carbohydrate-containing food groups. These interactions were further characterized using non-European UKB subsets and alternative measures of glycaemia (fasting glucose and follow-up HbA1c measurements). Our results highlight GDIs influencing HbA1c for future investigation, while reinforcing known challenges in detecting and replicating GDIs.
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Affiliation(s)
- Kenneth E Westerman
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Jenkai Miao
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of Genetics, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Medical and Population Genetics Program, Broad Institute, Cambridge, MA 02142, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Alisa K Manning
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Joanne B Cole
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA 02115, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
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41
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A genetic sum score of effect alleles associated with serum lipid concentrations interacts with educational attainment. Sci Rep 2021; 11:16541. [PMID: 34400708 PMCID: PMC8368036 DOI: 10.1038/s41598-021-95970-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 08/02/2021] [Indexed: 11/16/2022] Open
Abstract
High-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) levels are influenced by both genes and the environment. The aim was to investigate whether education and income as indicators of socioeconomic position (SEP) interact with lipid-increasing genetic effect allele scores (GES) in a population-based cohort. Using baseline data of 4516 study participants, age- and sex-adjusted linear regression models were fitted to investigate associations between GES and lipids stratified by SEP as well as including GES×SEP interaction terms. In the highest education group compared to the lowest stronger effects per GES standard deviation were observed for HDL-C (2.96 mg/dl [95%-CI: 2.19, 3.83] vs. 2.45 mg/dl [95%-CI: 1.12, 3.72]), LDL-C (6.57 mg/dl [95%-CI: 4.73, 8.37] vs. 2.66 mg/dl [95%-CI: −0.50, 5.76]) and TC (8.06 mg/dl [95%-CI: 6.14, 9.98] vs. 4.37 mg/dl [95%-CI: 0.94, 7.80]). Using the highest education group as reference, interaction terms showed indication of GES by low education interaction for LDL-C (ßGES×Education: −3.87; 95%-CI: −7.47, −0.32), which was slightly attenuated after controlling for GESLDL-C×Diabetes interaction (ßGES×Education: −3.42; 95%-CI: −6.98, 0.18). The present study showed stronger genetic effects on LDL-C in higher SEP groups and gave indication for a GESLDL-C×Education interaction, demonstrating the relevance of SEP for the expression of genetic health risks.
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42
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Bi W, Lee S. Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data. Front Genet 2021; 12:682638. [PMID: 34211504 PMCID: PMC8239389 DOI: 10.3389/fgene.2021.682638] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/17/2021] [Indexed: 02/05/2023] Open
Abstract
With the advances in genotyping technologies and electronic health records (EHRs), large biobanks have been great resources to identify novel genetic associations and gene-environment interactions on a genome-wide and even a phenome-wide scale. To date, several phenome-wide association studies (PheWAS) have been performed on biobank data, which provides comprehensive insights into many aspects of human genetics and biology. Although inspiring, PheWAS on large-scale biobank data encounters new challenges including computational burden, unbalanced phenotypic distribution, and genetic relationship. In this paper, we first discuss these new challenges and their potential impact on data analysis. Then, we summarize approaches that are scalable and robust in GWAS and PheWAS. This review can serve as a practical guide for geneticists, epidemiologists, and other medical researchers to identify genetic variations associated with health-related phenotypes in large-scale biobank data analysis. Meanwhile, it can also help statisticians to gain a comprehensive and up-to-date understanding of the current technical tool development.
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Affiliation(s)
- Wenjian Bi
- Department of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing, China
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
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Westerman KE, Pham DT, Hong L, Chen Y, Sevilla-González M, Sung YJ, Sun YV, Morrison AC, Chen H, Manning AK. CLUE: Exact maximal reduction of kinetic models by constrained lumping of differential equations. Bioinformatics 2021; 37:btab223. [PMID: 34037712 PMCID: PMC8545347 DOI: 10.1093/bioinformatics/btab223] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 03/09/2021] [Accepted: 04/07/2021] [Indexed: 01/19/2023] Open
Abstract
MOTIVATION Detailed mechanistic models of biological processes can pose significant challenges for analysis and parameter estimations due to the large number of equations used to track the dynamics of all distinct configurations in which each involved biochemical species can be found. Model reduction can help tame such complexity by providing a lower-dimensional model in which each macro-variable can be directly related to the original variables. RESULTS We present CLUE, an algorithm for exact model reduction of systems of polynomial differential equations by constrained linear lumping. It computes the smallest dimensional reduction as a linear mapping of the state space such that the reduced model preserves the dynamics of user-specified linear combinations of the original variables. Even though CLUE works with nonlinear differential equations, it is based on linear algebra tools, which makes it applicable to high-dimensional models. Using case studies from the literature, we show how CLUE can substantially lower model dimensionality and help extract biologically intelligible insights from the reduction. AVAILABILITY An implementation of the algorithm and relevant resources to replicate the experiments herein reported are freely available for download at https://github.com/pogudingleb/CLUE. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kenneth E Westerman
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Duy T Pham
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Liang Hong
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ye Chen
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Magdalena Sevilla-González
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63130, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Alisa K Manning
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
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Uncovering Evidence for Endocrine-Disrupting Chemicals That Elicit Differential Susceptibility through Gene-Environment Interactions. TOXICS 2021; 9:toxics9040077. [PMID: 33917455 PMCID: PMC8067468 DOI: 10.3390/toxics9040077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 03/27/2021] [Accepted: 04/02/2021] [Indexed: 12/17/2022]
Abstract
Exposure to endocrine-disrupting chemicals (EDCs) is linked to myriad disorders, characterized by the disruption of the complex endocrine signaling pathways that govern development, physiology, and even behavior across the entire body. The mechanisms of endocrine disruption involve a complex system of pathways that communicate across the body to stimulate specific receptors that bind DNA and regulate the expression of a suite of genes. These mechanisms, including gene regulation, DNA binding, and protein binding, can be tied to differences in individual susceptibility across a genetically diverse population. In this review, we posit that EDCs causing such differential responses may be identified by looking for a signal of population variability after exposure. We begin by summarizing how the biology of EDCs has implications for genetically diverse populations. We then describe how gene-environment interactions (GxE) across the complex pathways of endocrine signaling could lead to differences in susceptibility. We survey examples in the literature of individual susceptibility differences to EDCs, pointing to a need for research in this area, especially regarding the exceedingly complex thyroid pathway. Following a discussion of experimental designs to better identify and study GxE across EDCs, we present a case study of a high-throughput screening signal of putative GxE within known endocrine disruptors. We conclude with a call for further, deeper analysis of the EDCs, particularly the thyroid disruptors, to identify if these chemicals participate in GxE leading to differences in susceptibility.
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Blechter B, Wong JYY, Agnes Hsiung C, Hosgood HD, Yin Z, Shu XO, Zhang H, Shi J, Song L, Song M, Zheng W, Wang Z, Caporaso N, Burdette L, Yeager M, Berndt SI, Teresa Landi M, Chen CJ, Chang GC, Hsiao CF, Tsai YH, Chen KY, Huang MS, Su WC, Chen YM, Chien LH, Chen CH, Yang TY, Wang CL, Hung JY, Lin CC, Perng RP, Chen CY, Chen KC, Li YJ, Yu CJ, Chen YS, Chen YH, Tsai FY, Jie Seow W, Bassig BA, Hu W, Ji BT, Wu W, Guan P, He Q, Gao YT, Cai Q, Chow WH, Xiang YB, Lin D, Wu C, Wu YL, Shin MH, Hong YC, Matsuo K, Chen K, Pik Wong M, Lu D, Jin L, Wang JC, Seow A, Wu T, Shen H, Fraumeni JF, Yang PC, Chang IS, Zhou B, Chanock SJ, Rothman N, Chatterjee N, Lan Q. Sub-multiplicative interaction between polygenic risk score and household coal use in relation to lung adenocarcinoma among never-smoking women in Asia. ENVIRONMENT INTERNATIONAL 2021; 147:105975. [PMID: 33385923 PMCID: PMC8378844 DOI: 10.1016/j.envint.2020.105975] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 07/07/2020] [Accepted: 07/10/2020] [Indexed: 06/01/2023]
Abstract
We previously identified 10 lung adenocarcinoma susceptibility loci in a genome-wide association study (GWAS) conducted in the Female Lung Cancer Consortium in Asia (FLCCA), the largest genomic study of lung cancer among never-smoking women to date. Furthermore, household coal use for cooking and heating has been linked to lung cancer in Asia, especially in Xuanwei, China. We investigated the potential interaction between genetic susceptibility and coal use in FLCCA. We analyzed GWAS-data from Taiwan, Shanghai, and Shenyang (1472 cases; 1497 controls), as well as a separate study conducted in Xuanwei (152 cases; 522 controls) for additional analyses. We summarized genetic susceptibility using a polygenic risk score (PRS), which was the weighted sum of the risk-alleles from the 10 previously identified loci. We estimated associations between a PRS, coal use (ever/never), and lung adenocarcinoma with multivariable logistic regression models, and evaluated potential gene-environment interactions using likelihood ratio tests. There was a strong association between continuous PRS and lung adenocarcinoma among never coal users (Odds Ratio (OR) = 1.69 (95% Confidence Interval (CI) = 1.53, 1.87), p=1 × 10-26). This effect was attenuated among ever coal users (OR = 1.24 (95% CI: 1.03, 1.50), p = 0.02, p-interaction = 6 × 10-3). We observed similar attenuation among coal users from Xuanwei. Our study provides evidence that genetic susceptibility to lung adenocarcinoma among never-smoking Asian women is weaker among coal users. These results suggest that lung cancer pathogenesis may differ, at least partially, depending on exposure to coal combustion products. Notably, these novel findings are among the few instances of sub-multiplicative gene-environment interactions in the cancer literature.
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Affiliation(s)
- Batel Blechter
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Jason Y Y Wong
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Chao Agnes Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - H Dean Hosgood
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Zhihua Yin
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, People's Republic of China
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
| | - Han Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Minsun Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA; Department of Statistics, Sookmyung Women's University, Seoul, Republic of Korea
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
| | - Zhaoming Wang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA; Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Neil Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Laurie Burdette
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA; Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, USA; Leidos Biomedical Research, Inc., Frederick, MD, USA
| | - Meredith Yeager
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Chien-Jen Chen
- Genomic Research Center, Academia Sinica, Taipei, Taiwan
| | - Gee-Chen Chang
- Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan; Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chin-Fu Hsiao
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Ying-Huang Tsai
- Division of Pulmonary and Critical Care Medicine, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan
| | - Kuan-Yu Chen
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Ming-Shyan Huang
- Department of Internal Medicine, E-Da Cancer Hospital, School of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Wu-Chou Su
- Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan; College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Li-Hsin Chien
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Chung-Hsing Chen
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Tsung-Ying Yang
- Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chih-Liang Wang
- Department of Pulmonary and Critical Care, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Jen-Yu Hung
- Department of Internal Medicine, Kaohsiung Medical University Hospital, School of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chien-Chung Lin
- Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Reury-Perng Perng
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chih-Yi Chen
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan; Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Kun-Chieh Chen
- Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Yao-Jen Li
- Genomic Research Center, Academia Sinica, Taipei, Taiwan
| | - Chong-Jen Yu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Yi-Song Chen
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Ying-Hsiang Chen
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Fang-Yu Tsai
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Wei Jie Seow
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Bryan A Bassig
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Wei Hu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Bu-Tian Ji
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Wei Wu
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, People's Republic of China
| | - Peng Guan
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, People's Republic of China
| | - Qincheng He
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, People's Republic of China
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, People's Republic of China
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center, Nashville, TN, USA
| | - Wong-Ho Chow
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yong-Bing Xiang
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, People's Republic of China
| | - Dongxin Lin
- Department of Etiology & Carcinogenesis and State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Chen Wu
- Department of Etiology & Carcinogenesis and State Key Laboratory of Molecular Oncology, Cancer Institute and Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Yi-Long Wu
- Guangdong Lung Cancer Institute, Medical Research Center and Cancer Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China
| | - Min-Ho Shin
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Keitaro Matsuo
- Division of Molecular Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, Tianjin, People's Republic of China
| | - Maria Pik Wong
- Department of Pathology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Daru Lu
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, People's Republic of China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, People's Republic of China
| | - Li Jin
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, People's Republic of China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, People's Republic of China
| | - Jiu-Cun Wang
- Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, People's Republic of China; State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, People's Republic of China
| | - Adeline Seow
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Tangchun Wu
- Department of Occupational and Environmental Health and Ministry of Education Key Lab for Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China; Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Joseph F Fraumeni
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Pan-Chyr Yang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - I-Shou Chang
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Baosen Zhou
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, People's Republic of China
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
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Majumdar A, Burch KS, Haldar T, Sankararaman S, Pasaniuc B, Gauderman WJ, Witte JS. A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. Bioinformatics 2021; 36:5640-5648. [PMID: 33453114 DOI: 10.1093/bioinformatics/btaa1083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 12/09/2020] [Accepted: 12/17/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes. RESULTS Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e., GxE pleiotropy), our approach offers substantial gain in power (18%-43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two loci with an overall GxE effect on the vector of lipids, one of which was missed by the competing approaches. AVAILABILITY We provide an R package MPGE implementing the proposed approach which is available from CRAN: https://cran.r-project.org/web/packages/MPGE/index.html.
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Affiliation(s)
- Arunabha Majumdar
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Kathryn S Burch
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.,Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - W James Gauderman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
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Ghazarian AA, Simonds NI, Lai GY, Mechanic LE. Opportunities for Gene and Environment Research in Cancer: An Updated Review of NCI's Extramural Grant Portfolio. Cancer Epidemiol Biomarkers Prev 2020; 30:576-583. [PMID: 33323360 DOI: 10.1158/1055-9965.epi-20-1264] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 10/28/2020] [Accepted: 12/11/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The study of gene-environment (GxE) interactions is a research priority for the NCI. Previously, our group analyzed NCI's extramural grant portfolio from fiscal years (FY) 2007 to 2009 to determine the state of the science in GxE research. This study builds upon our previous effort and examines changes in the landscape of GxE cancer research funded by NCI. METHODS The NCI grant portfolio was examined from FY 2010 to 2018 using the iSearch application. A time-trend analysis was conducted to explore changes over the study interval. RESULTS A total of 107 grants met the search criteria and were abstracted. The most common cancer types studied were breast (19.6%) and colorectal (18.7%). Most grants focused on GxE using specific candidate genes (69.2%) compared with agnostic approaches using genome-wide (26.2%) or whole-exome/whole-genome next-generation sequencing (NGS) approaches (19.6%); some grants used more than one approach to assess genetic variation. More funded grants incorporated NGS technologies in FY 2016-2018 compared with prior FYs. Environmental exposures most commonly examined were energy balance (46.7%) and drugs/treatment (40.2%). Over the time interval, we observed a decrease in energy balance applications with a concurrent increase in drug/treatment applications. CONCLUSIONS Research in GxE interactions has continued to concentrate on common cancers, while there have been some shifts in focus of genetic and environmental exposures. Opportunities exist to study less common cancers, apply new technologies, and increase racial/ethnic diversity. IMPACT This analysis of NCI's extramural grant portfolio updates previous efforts and provides a review of NCI grant support for GxE research.
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Affiliation(s)
- Armen A Ghazarian
- Environmental Epidemiology Branch, Epidemiology and Genomics Research Program (EGRP), Division of Cancer Control and Population Sciences (DCCPS), NCI, Bethesda, Maryland
| | | | - Gabriel Y Lai
- Environmental Epidemiology Branch, Epidemiology and Genomics Research Program (EGRP), Division of Cancer Control and Population Sciences (DCCPS), NCI, Bethesda, Maryland
| | - Leah E Mechanic
- Genomic Epidemiology Branch, EGRP, DCCPS, NCI, Bethesda, Maryland.
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48
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Mbemi A, Khanna S, Njiki S, Yedjou CG, Tchounwou PB. Impact of Gene-Environment Interactions on Cancer Development. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E8089. [PMID: 33153024 PMCID: PMC7662361 DOI: 10.3390/ijerph17218089] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 10/26/2020] [Accepted: 10/30/2020] [Indexed: 12/24/2022]
Abstract
Several epidemiological and experimental studies have demonstrated that many human diseases are not only caused by specific genetic and environmental factors but also by gene-environment interactions. Although it has been widely reported that genetic polymorphisms play a critical role in human susceptibility to cancer and other chronic disease conditions, many single nucleotide polymorphisms (SNPs) are caused by somatic mutations resulting from human exposure to environmental stressors. Scientific evidence suggests that the etiology of many chronic illnesses is caused by the joint effect between genetics and the environment. Research has also pointed out that the interactions of environmental factors with specific allelic variants highly modulate the susceptibility to diseases. Hence, many scientific discoveries on gene-environment interactions have elucidated the impact of their combined effect on the incidence and/or prevalence rate of human diseases. In this review, we provide an overview of the nature of gene-environment interactions, and discuss their role in human cancers, with special emphases on lung, colorectal, bladder, breast, ovarian, and prostate cancers.
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Affiliation(s)
- Ariane Mbemi
- NIH/NIMHD RCMI-Center for Health Disparities Research, Jackson State University, 1400 Lynch Street, Box 18750, Jackson, MS 39217, USA; (A.M.); (S.N.)
- Department of Biology, College of Science, Engineering and Technology, Jackson State University, 1400 Lynch Street, Box 18540, Jackson, MS 39217, USA
| | - Sunali Khanna
- Department of Oral Medicine and Radiology, Nair Hospital Dental College, Municipal Corporation of Greater Mumbai, Mumbai 400 008, India;
| | - Sylvianne Njiki
- NIH/NIMHD RCMI-Center for Health Disparities Research, Jackson State University, 1400 Lynch Street, Box 18750, Jackson, MS 39217, USA; (A.M.); (S.N.)
- Department of Biology, College of Science, Engineering and Technology, Jackson State University, 1400 Lynch Street, Box 18540, Jackson, MS 39217, USA
| | - Clement G. Yedjou
- Department of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd., Tallahassee, FL 32307, USA;
| | - Paul B. Tchounwou
- NIH/NIMHD RCMI-Center for Health Disparities Research, Jackson State University, 1400 Lynch Street, Box 18750, Jackson, MS 39217, USA; (A.M.); (S.N.)
- Department of Biology, College of Science, Engineering and Technology, Jackson State University, 1400 Lynch Street, Box 18540, Jackson, MS 39217, USA
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49
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Adams CJ, Wu SL, Shao X, Bradshaw PT, Gonzales E, Smith JB, Xiang AH, Bellesis KH, Chinn T, Bos SD, Wendel-Haga M, Olsson T, Kockum I, Langer-Gould AM, Schaefer C, Alfredsson L, Barcellos LF. Pregnancy does not modify the risk of MS in genetically susceptible women. NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION 2020; 7:7/6/e898. [PMID: 33037103 PMCID: PMC7673284 DOI: 10.1212/nxi.0000000000000898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 08/25/2020] [Indexed: 11/16/2022]
Abstract
Objective To use the case-only gene-environment (G E) interaction study design to estimate interaction between pregnancy before onset of MS symptoms and established genetic risk factors for MS among White adult females. Methods We studied 2,497 female MS cases from 4 cohorts in the United States, Sweden, and Norway with clinical, reproductive, and genetic data. Pregnancy exposure was defined in 2 ways: (1) live birth pregnancy before onset of MS symptoms and (2) parity before onset of MS symptoms. We estimated interaction between pregnancy exposure and established genetic risk variants, including a weighted genetic risk score and both HLA and non-HLA variants, using logistic regression and proportional odds regression within each cohort. Within-cohort associations were combined using inverse variance meta-analyses with random effects. The case-only G × E independence assumption was tested in 7,067 individuals without MS. Results Evidence for interaction between pregnancy exposure and established genetic risk variants, including the strongly associated HLA-DRB1*15:01 allele and a weighted genetic risk score, was not observed. Results from sensitivity analyses were consistent with observed results. Conclusion Our findings indicate that pregnancy before symptom onset does not modify the risk of MS in genetically susceptible White females.
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Affiliation(s)
- Cameron J Adams
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden.
| | - Sean L Wu
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Xiaorong Shao
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Patrick T Bradshaw
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Edlin Gonzales
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Jessica B Smith
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Anny H Xiang
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Kalliope H Bellesis
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Terrence Chinn
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Steffan D Bos
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Marte Wendel-Haga
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Tomas Olsson
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Ingrid Kockum
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Annette M Langer-Gould
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Catherine Schaefer
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Lars Alfredsson
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
| | - Lisa F Barcellos
- From the Divisions of Epidemiology and Biostatistics (C.J.A., S.L.W.), School of Public Health, University of California, Berkeley, Berkeley, CA; Genetic Epidemiology and Genomics Laboratory (X.S., L.F.B.), University of California, Berkeley, Berkeley, CA; School of Public Health (P.T.B.), University of California, Berkeley, Berkeley, CA, USA; Department of Research & Evaluation (E.G., J.B.S., A.H.X.), Kaiser Permanente Southern California, Los Angeles, CA; Kaiser Permanente Division of Research (K.H.B., T.C., C.S.), Kaiser Permanente Northern California, Oakland, CA; University of Oslo (S.D.B.), Institute of Clinical Medicine & Oslo University Hospital, Department of Neurology, Oslo, Norway; Oslo University Hospital (M.W.-H.), Department of Neurology, Oslo, Norway; Department of Clinical Neuroscience (T.O.), Karolinska Instituet, Stockholm, Sweden; Department of Clinical Neuroscience (I.K.), Karolinska Institutet, Stockholm, Sweden; Southern California Permanente Medical Group/Kaiser Permanente (A.M.L.-G.), Department of Neurology, Los Angeles, CA; and Institute of Environmental Medicine (L.A.), Karolinska Institutet and Centre for Occupational and Environmental Medicine, Region Stockholm, Stockholm, Sweden
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50
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Tang H, Jiang L, Stolzenberg-Solomon RZ, Arslan AA, Beane Freeman LE, Bracci PM, Brennan P, Canzian F, Du M, Gallinger S, Giles GG, Goodman PJ, Kooperberg C, Le Marchand L, Neale RE, Shu XO, Visvanathan K, White E, Zheng W, Albanes D, Andreotti G, Babic A, Bamlet WR, Berndt SI, Blackford A, Bueno-de-Mesquita B, Buring JE, Campa D, Chanock SJ, Childs E, Duell EJ, Fuchs C, Gaziano JM, Goggins M, Hartge P, Hassam MH, Holly EA, Hoover RN, Hung RJ, Kurtz RC, Lee IM, Malats N, Milne RL, Ng K, Oberg AL, Orlow I, Peters U, Porta M, Rabe KG, Rothman N, Scelo G, Sesso HD, Silverman DT, Thompson IM, Tjønneland A, Trichopoulou A, Wactawski-Wende J, Wentzensen N, Wilkens LR, Yu H, Zeleniuch-Jacquotte A, Amundadottir LT, Jacobs EJ, Petersen GM, Wolpin BM, Risch HA, Chatterjee N, Klein AP, Li D, Kraft P, Wei P. Genome-Wide Gene-Diabetes and Gene-Obesity Interaction Scan in 8,255 Cases and 11,900 Controls from PanScan and PanC4 Consortia. Cancer Epidemiol Biomarkers Prev 2020; 29:1784-1791. [PMID: 32546605 PMCID: PMC7483330 DOI: 10.1158/1055-9965.epi-20-0275] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/06/2020] [Accepted: 06/09/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level. METHODS We conducted a gene-environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I-III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case-control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics. RESULTS No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 × 10-6) was observed in the meta-analysis (P GxE = 1.2 ×10-6, P Joint = 4.2 ×10-7). CONCLUSIONS This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans. IMPACT This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.
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Affiliation(s)
- Hongwei Tang
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lai Jiang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | | | - Alan A Arslan
- Department of Obstetrics and Gynecology, New York University School of Medicine, New York, New York
- Department of Population Health, New York University School of Medicine, New York, New York
- Department of Environmental Medicine, New York University School of Medicine, New York, New York
| | | | - Paige M Bracci
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California
| | - Paul Brennan
- International Agency for Research on Cancer, Lyon, France
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mengmeng Du
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Steven Gallinger
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System and University of Toronto, Toronto, Ontario, Canada
| | - Graham G Giles
- Division of Cancer Epidemiology, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Phyllis J Goodman
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Loïc Le Marchand
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Rachel E Neale
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Emily White
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | | | - Ana Babic
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - William R Bamlet
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Amanda Blackford
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Bas Bueno-de-Mesquita
- Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
- Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, the Netherlands
- Department of Epidemiology and Biostatistics, The School of Public Health, Imperial College London, London, United Kingdom
- Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Julie E Buring
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Daniele Campa
- Department of Biology, University of Pisa, Pisa, Italy
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Erica Childs
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Eric J Duell
- Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Charles Fuchs
- Yale Cancer Center, New Haven, Connecticut
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut
- Smilow Cancer Hospital, New Haven, Connecticut
| | - J Michael Gaziano
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Boston Veteran Affairs Healthcare System, Boston, Massachusetts
| | - Michael Goggins
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Patricia Hartge
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Manal H Hassam
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Elizabeth A Holly
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California
| | - Robert N Hoover
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System and University of Toronto, Toronto, Ontario, Canada
| | - Robert C Kurtz
- Gastroenterology, Hepatology, and Nutrition Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - I-Min Lee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Núria Malats
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre, Madrid, Spain
| | - Roger L Milne
- Division of Cancer Epidemiology, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Kimmie Ng
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Ann L Oberg
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Irene Orlow
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ulrike Peters
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Miquel Porta
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- Hospital del Mar Institute of Medical Research (IMIM), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Kari G Rabe
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | | | - Howard D Sesso
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Debra T Silverman
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Ian M Thompson
- CHRISTUS Santa Rosa Hospital - Medical Center, San Antonio, Texas
| | - Anne Tjønneland
- Department of Public Health, University of Copenhagen and Danish Cancer Society Research Center Diet, Genes and Environment, Copenhagen, Denmark
| | - Antonia Trichopoulou
- Hellenic Health Foundation, World Health Organization Collaborating Center of Nutrition, Medical School, University of Athens, Athens, Greece
| | - Jean Wactawski-Wende
- Department of Epidemiology and Environmental Health, University of Buffalo, Buffalo, New York
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Lynne R Wilkens
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Herbert Yu
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Anne Zeleniuch-Jacquotte
- Department of Population Health, New York University School of Medicine, New York, New York
- Department of Environmental Medicine, New York University School of Medicine, New York, New York
| | | | - Eric J Jacobs
- Department of Public Health, University of Copenhagen and Danish Cancer Society Research Center Diet, Genes and Environment, Copenhagen, Denmark
| | - Gloria M Petersen
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Brian M Wolpin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Harvey A Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alison P Klein
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Donghui Li
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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